System and method for content provisioning with dual recommendation engines

ABSTRACT

Systems and methods for content selection with first and second recommendation engines are disclosed herein. The system can include a memory include a content library database and a model database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include one or more servers that can include a packet selection system and a presentation system. These one or more servers can: receive response data from the user device; provide received response data to a first recommendation engine; alert a second recommendation engine when a selected next node is a placeholder node; retrieve at least one statistical model relevant to selection of next node content; and select next node content based on an output of the at least one statistical model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a claims the benefit of U.S. Provisional ApplicationNo. 62/320,213, filed on Apr. 8, 2016, and entitled “ADAPTIVE PATHWAYSAND COGNITIVE TUTORING”, the entirety of which is hereby incorporated byreference herein.

BACKGROUND

A computer network or data network is a telecommunications network whichallows computers to exchange data. In computer networks, networkedcomputing devices exchange data with each other along network links(data connections). The connections between nodes are established usingeither cable media or wireless media. The best-known computer network isthe Internet.

Network computer devices that originate, route and terminate the dataare called network nodes. Nodes can include hosts such as personalcomputers, phones, servers as well as networking hardware. Two suchdevices can be said to be networked together when one device is able toexchange information with the other device, whether or not they have adirect connection to each other.

Computer networks differ in the transmission media used to carry theirsignals, the communications protocols to organize network traffic, thenetwork's size, topology and organizational intent. In most cases,communications protocols are layered on other more specific or moregeneral communications protocols, except for the physical layer thatdirectly deals with the transmission media.

BRIEF SUMMARY

One aspect of the present disclosure relates to a system for contentselection with first and second recommendation engines. In someembodiments, the system can include a memory. The memory can include: acontent library database and a model database. In some embodiments, thecontent library database can include a plurality of nodes arranged in acontent network. In some embodiments, the nodes in the content networkare pairwise connected via a plurality of edges. In some embodiments,some of the nodes in the content network are associated with a datapacket and a guard condition. In some embodiments, some of the nodes inthe content network are associated with a database of placeholdercontent. In some embodiments, the model database can include a pluralityof models relating to at least one of: a user skill level or a datapacket difficulty level, which models can be, for example, statisticalmodels. The system can include a user device. The user device caninclude a first network interface that can exchange data via acommunication network. The user device can include a first I/O subsystemthat can convert electrical signals to user interpretable outputs via auser interface. The system can include one or more servers. These one ormore servers can include a packet selection system and a presentationsystem. In some embodiments, the one or more servers can be controlledaccording to software executed by the one or more servers. In someembodiments, this software can control the server to: receive responsedata from the user device; provide received response data to a firstrecommendation engine, and which first recommendation engine can selecta next node based on: a current location in the content network,potential next nodes, past response data, and one or several guardconditions associated with the potential next nodes. In someembodiments, the processor can be controlled to: alert a secondrecommendation engine when a selected next node includes a placeholdernode, which placeholder node is associated with a database ofplaceholder content; retrieve at least one model relevant to selectionof next node content; and select next node content based on an output ofthe at least one model.

In some embodiments, providing received response data to the firstrecommendation engine further includes selecting a next node. In someembodiments, selecting the next node includes: identifying potentialnext nodes; and retrieving guard conditions. In some embodiments, eachguard condition defines one or several prerequisites for entry into oneof the potential next nodes.

In some embodiments, identifying potential next nodes includes:identifying the user's location in the content network, which locationin the content network is at an origin node; identifying edges extendingfrom the origin node; and identifying non-prerequisite nodes connectedto the origin node via the identified edges. In some embodiments,selecting the next node further includes: identifying the userassociated with the user device; and retrieving the user history fromthe memory.

In some embodiments, selecting the next node further includesapplication of the user history to the guard conditions of the potentialnext nodes. In some embodiments, application of the user history to theguard conditions of the potential next nodes includes: (a) selecting oneof the potential next nodes; (b) identification of a guard conditionassociated with the selected potential next node; (c) comparing theguard condition with the user history; and (d) associating a first valuewith the selected one of the potential next nodes when the comparison ofthe guard condition with the user history indicates that the guardcondition is met. In some embodiments, steps (a)-(d) are repeated foreach of the potential next nodes.

In some embodiments, selecting next node content based on an output ofthe at least one model includes: identifying the user associated withthe user device; retrieving the user history; and identifying potentialnext node content. In some embodiments, selecting next node contentbased on an output of the at least one model includes: identifying oneor several features of at least one of: the potential next node contentor the user history; extracting the identified one or several features;and inputting some or all of the one or several features into theretrieved at least one model. In some embodiments, selecting next nodecontent based on an output of the at least one model includes generatingan output with the retrieved at least one model based on the input ofsome or all of the one or several features.

In some embodiments, the one or more servers are further configured toprovide the selected next node content to the user device. In someembodiments, providing the selected next node content to the user deviceincludes: generating a plurality of electrical signals containing theselected next node content; and sending the electrical signal to theuser device. In some embodiments, the first recommendation engine iscontained in the presentation system and wherein the secondrecommendation engine is contained in the packet selection system.

One aspect of the present disclosure relates to a method for contentselection with first and second recommendation engines. The methodincludes: receiving response data from a user device at one or moreservers including a packet selection system and a presentation system;automatically providing received response data to a first recommendationengine, which first recommendation engine can select a next node basedon at least one of: a current location in the content network; potentialnext nodes; past response data; or one or several guard conditionsassociated with the potential next nodes; and alerting a secondrecommendation engine when a selected next node is a placeholder node.In some embodiments, the placeholder node is associated with a databaseof placeholder content. The method can include: retrieving at least onemodel relevant to selection of next node content from a model databaseincluding a plurality of models relating to at least one of: a userskill level or a data packet difficulty level; and selecting next nodecontent based on an output of the at least one model.

In some embodiments, the method can include providing the selected nextnode content to the user device. In some embodiments, providing theselected next node content to the user device includes: generating aplurality of electrical signals containing or encoding the selected nextnode content; and sending the electrical signal to the user device. Insome embodiments, the first recommendation engine is in the presentationsystem and the second recommendation engine is in the packet selectionsystem.

In some embodiments, selecting next node content based on an output ofthe at least one model includes: identifying the user associated withthe user device; retrieving the user history; and identifying potentialnext node content. In some embodiments, selecting next node contentbased on an output of the at least one model includes: identifying oneor several features of at least one of: the potential next node contentor the user history; extracting the identified one or several features;and inputting some or all of the one or several features into theretrieved at least one model.

One aspect of the present disclosure relate to a method of hybridcontent provisioning. The method includes: receiving user identificationinformation, which user identification information identifies a user;determining the location of the user in a content network comprising aplurality of nodes linked in a sequential relationship; identifyingpotential next nodes based on the location of the user in the contentnetwork; determining a next node from the potential next nodes; andproviding next content to the user, which next content is directlylinked to the next node when the next node is not a placeholder node,and which next content is provided from a database of placeholdercontent associated with determined next node when the next node is aplaceholder node. In some embodiments, some or all of the nodes are eachassociated with a guard condition. In some embodiments, each guardcondition identifies a condition for entrance into the node associatedwith that guard condition.

One aspect of the present disclosure relates to a method of contentprovisioning with distributed presentation engines. The method includes:receiving a data packet at a presenter module in at least one server;extracting packet metadata from the received data packet, which packetmetadata identifies at least one attribute of the received data packet;providing delivery portions of the received data packet to a view modulelocated in a user device; receiving a data request at the presentermodule from the view module; retrieving a second data packetcorresponding to the data request at the presenter module; and receivinga response to the provided delivery portion of the received data packet.

One aspect of the present disclosure relates to a method for automaticdata packet. The method includes: identifying a user cohort, which usercohort includes a plurality of user sharing a common attribute;providing a data packet to the users in the user cohort; receivingresponses to the provided data packet from the user in the user cohort;translating each of the received responses into an observable, whichobservable includes a characterization of the received response;generating response cohorts including groups of users in the usercohort, which response cohorts are generated based on commonalities inthe observables of the received responses; identifying a misconceptionbased on the comparison of a cohort percentage and a misconceptionthreshold, and generating and sending an alert to a user devicerequesting modification of at least one data packet associated with themisconception.

One aspect of the present disclosure relates to a method for automatictriggering of a targeted intervention. The method includes: selecting auser with at least one server; receiving user metadata associated withthe identified user from a user profile database in a memory at the atleast one server; determining that the user is associated with apotential misconception; retrieving metadata of the selected userindicative of the presence or absence of the potential misconceptionwith which the user is associated; determining that the potentialmisconception is not confirmed in the user based on a comparison of amisconception threshold and metadata indicative of the presence orabsence of the potential misconception with which the user isassociated; updating the user metadata based on responses received fromthe user and relating to a plurality of provided supplemental datapackets in the user profile database; confirming the presence of themisconception based on the updated user metadata; and automaticallytriggering a targeted intervention. In some embodiments, the potentialmisconception can be identified based on the association of the userwith a cohort linked with a misconception, which cohort can be a groupof users having common attributes of observables generated from one orseveral responses provided by the users in the cohort. In someembodiments, the intervention can include interaction with an AI agent.The AI agent communicatingly linked with the user device via acommunication network.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples, whileindicating various embodiments, are intended for purposes ofillustration only and are not intended to necessarily limit the scope ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example of a contentdistribution network.

FIG. 2 is a block diagram illustrating a computer server and computingenvironment within a content distribution network.

FIG. 3 is a block diagram illustrating an embodiment of one or more datastore servers within a content distribution network.

FIG. 4 is a block diagram illustrating an embodiment of one or morecontent management servers within a content distribution network.

FIG. 5 is a block diagram illustrating the physical and logicalcomponents of a special-purpose computer device within a contentdistribution network.

FIG. 6 is a block diagram illustrating one embodiment of thecommunication network.

FIG. 7 is a block diagram illustrating one embodiment of user device andsupervisor device communication.

FIG. 8 is a schematic illustration of one embodiment of a computingstack.

FIG. 9A is a schematic illustration of an embodiment of communicationand processing flow of modules within the content distribution network.

FIG. 9B is a schematic illustration of another embodiment ofcommunication and processing flow of modules within the contentdistribution network.

9C is a schematic illustration of an additional embodiment ofcommunication and processing flow of modules within the contentdistribution network.

FIG. 9D is a schematic illustration of one embodiment of thepresentation process.

FIG. 10A is a flowchart illustrating one embodiment of a process fordata management.

FIG. 10B is a flowchart illustrating one embodiment of a process forevaluating a response.

FIG. 11 is a schematic illustration of one embodiment of the contentnetwork.

FIG. 12 is a flowchart illustrating one embodiment of a process forproviding content to a user with two recommendation engines.

FIG. 13 is a flowchart illustrating one embodiment of a process foridentifying the next node.

FIG. 14 is a flowchart illustrating one embodiment of a process forselecting next node content.

FIG. 15 is a flowchart illustrating one embodiment of a process forselecting next node content based on the determination of the presenceof a placeholder node.

FIG. 16 is a flowchart illustrating one embodiment of a process foroperation of a two-part presentation engine.

FIG. 17 is a flowchart illustrating one embodiment of a process forautomated misconception identification and remediation.

FIG. 18 is a flowchart illustrating one embodiment of a process foridentifying a user misconception.

FIG. 19 is a flowchart illustrating one embodiment of a process forautomatically triggering an intervention.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides illustrative embodiment(s) only and isnot intended to limit the scope, applicability or configuration of thedisclosure. Rather, the ensuing description of the illustrativeembodiment(s) will provide those skilled in the art with an enablingdescription for implementing a preferred exemplary embodiment. It isunderstood that various changes can be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

This application is related to U.S. application Ser. No. 15/236,196,filed on Aug. 12, 2016, and entitled SYSTEMS AND METHODS OF EVENT-BASEDCONTENT PROVISIONING; U.S. application Ser. No. 15/236,103, filed onAug. 12, 2016, and entitled SYSTEMS AND METHODS FOR DATA PACKET METADATASTABILIZATION; and U.S. application Ser. No. 15/236,275, filed on Aug.12, 2016, and entitled SYSTEM AND METHOD FOR AUTOMATIC CONTENTAGGREGATION GENERATION, the entirety of each of which is herebyincorporated by reference herein.

With reference now to FIG. 1, a block diagram is shown illustratingvarious components of a content distribution network (CDN) 100 whichimplements and supports certain embodiments and features describedherein. In some embodiments, the content distribution network 100 cancomprise one or several physical components and/or one or severalvirtual components such as, for example, one or several cloud computingcomponents. In some embodiments, the content distribution network 100can comprise a mixture of physical and cloud computing components.

Content distribution network 100 may include one or more contentmanagement servers 102. As discussed below in more detail, contentmanagement servers 102 may be any desired type of server including, forexample, a rack server, a tower server, a miniature server, a bladeserver, a mini rack server, a mobile server, an ultra-dense server, asuper server, or the like, and may include various hardware components,for example, a motherboard, a processing unit, memory systems, harddrives, network interfaces, power supplies, etc. Content managementserver 102 may include one or more server farms, clusters, or any otherappropriate arrangement and/or combination of computer servers. Contentmanagement server 102 may act according to stored instructions locatedin a memory subsystem of the server 102, and may run an operatingsystem, including any commercially available server operating systemand/or any other operating systems discussed herein.

The content distribution network 100 may include one or more data storeservers 104, such as database servers and file-based storage systems.The database servers 104 can access data that can be stored on a varietyof hardware components. These hardware components can include, forexample, components forming tier 0 storage, components forming tier 1storage, components forming tier 2 storage, and/or any other tier ofstorage. In some embodiments, tier 0 storage refers to storage that isthe fastest tier of storage in the database server 104, andparticularly, the tier 0 storage is the fastest storage that is not RAMor cache memory. In some embodiments, the tier 0 memory can be embodiedin solid state memory such as, for example, a solid-state drive (SSD)and/or flash memory.

In some embodiments, the tier 1 storage refers to storage that is one orseveral higher performing systems in the memory management system, andthat is relatively slower than tier 0 memory, and relatively faster thanother tiers of memory. The tier 1 memory can be one or several harddisks that can be, for example, high-performance hard disks. These harddisks can be one or both of physically or communicatingly connected suchas, for example, by one or several fiber channels. In some embodiments,the one or several disks can be arranged into a disk storage system, andspecifically can be arranged into an enterprise class disk storagesystem. The disk storage system can include any desired level ofredundancy to protect data stored therein, and in one embodiment, thedisk storage system can be made with grid architecture that createsparallelism for uniform allocation of system resources and balanced datadistribution.

In some embodiments, the tier 2 storage refers to storage that includesone or several relatively lower performing systems in the memorymanagement system, as compared to the tier 1 and tier 2 storages. Thus,tier 2 memory is relatively slower than tier 1 and tier 0 memories. Tier2 memory can include one or several SATA-drives or one or severalNL-SATA drives.

In some embodiments, the one or several hardware and/or softwarecomponents of the database server 104 can be arranged into one orseveral storage area networks (SAN), which one or several storage areanetworks can be one or several dedicated networks that provide access todata storage, and particularly that provide access to consolidated,block level data storage. A SAN typically has its own network of storagedevices that are generally not accessible through the local area network(LAN) by other devices. The SAN allows access to these devices in amanner such that these devices appear to be locally attached to the userdevice.

Data stores 104 may comprise stored data relevant to the functions ofthe content distribution network 100. Illustrative examples of datastores 104 that may be maintained in certain embodiments of the contentdistribution network 100 are described below in reference to FIG. 3. Insome embodiments, multiple data stores may reside on a single server104, either using the same storage components of server 104 or usingdifferent physical storage components to assure data security andintegrity between data stores. In other embodiments, each data store mayhave a separate dedicated data store server 104.

Content distribution network 100 also may include one or more userdevices 106 and/or supervisor devices 110. User devices 106 andsupervisor devices 110 may display content received via the contentdistribution network 100, and may support various types of userinteractions with the content. User devices 106 and supervisor devices110 may include mobile devices such as smartphones, tablet computers,personal digital assistants, and wearable computing devices. Such mobiledevices may run a variety of mobile operating systems, and may beenabled for Internet, e-mail, short message service (SMS), Bluetooth®,mobile radio-frequency identification (M-RFID), and/or othercommunication protocols. Other user devices 106 and supervisor devices110 may be general purpose personal computers or special-purposecomputing devices including, by way of example, personal computers,laptop computers, workstation computers, projection devices, andinteractive room display systems. Additionally, user devices 106 andsupervisor devices 110 may be any other electronic devices, such asthin-client computers, Internet-enabled gaming systems, business or homeappliances, and/or personal messaging devices, capable of communicatingover network(s) 120.

In different contexts of content distribution networks 100, user devices106 and supervisor devices 110 may correspond to different types ofspecialized devices, for example, student devices and teacher devices inan educational network, employee devices and presentation devices in acompany network, different gaming devices in a gaming network, etc. Insome embodiments, user devices 106 and supervisor devices 110 mayoperate in the same physical location 107, such as a classroom orconference room. In such cases, the devices may contain components thatsupport direct communications with other nearby devices, such as awireless transceivers and wireless communications interfaces, Ethernetsockets or other Local Area Network (LAN) interfaces, etc. In otherimplementations, the user devices 106 and supervisor devices 110 neednot be used at the same location 107, but may be used in remotegeographic locations in which each user device 106 and supervisor device110 may use security features and/or specialized hardware (e.g.,hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.) tocommunicate with the content management server 102 and/or other remotelylocated user devices 106. Additionally, different user devices 106 andsupervisor devices 110 may be assigned different designated roles, suchas presenter devices, teacher devices, administrator devices, or thelike, and in such cases the different devices may be provided withadditional hardware and/or software components to provide content andsupport user capabilities not available to the other devices.

The content distribution network 100 also may include a privacy server108 that maintains private user information at the privacy server 108while using applications or services hosted on other servers. Forexample, the privacy server 108 may be used to maintain private data ofa user within one jurisdiction even though the user is accessing anapplication hosted on a server (e.g., the content management server 102)located outside the jurisdiction. In such cases, the privacy server 108may intercept communications between a user device 106 or supervisordevice 110 and other devices that include private user information. Theprivacy server 108 may create a token or identifier that does notdisclose the private information and may use the token or identifierwhen communicating with the other servers and systems, instead of usingthe user's private information.

As illustrated in FIG. 1, the content management server 102 may be incommunication with one or more additional servers, such as a contentserver 112, a user data server 112, and/or an administrator server 116.Each of these servers may include some or all of the same physical andlogical components as the content management server(s) 102, and in somecases, the hardware and software components of these servers 112-116 maybe incorporated into the content management server(s) 102, rather thanbeing implemented as separate computer servers.

Content server 112 may include hardware and software components togenerate, store, and maintain the content resources for distribution touser devices 106 and other devices in the network 100. For example, incontent distribution networks 100 used for professional training andeducational purposes, content server 112 may include data stores oftraining materials, presentations, plans, syllabi, reviews, evaluations,interactive programs and simulations, course models, course outlines,and various training interfaces that correspond to different materialsand/or different types of user devices 106. In content distributionnetworks 100 used for media distribution, interactive gaming, and thelike, a content server 112 may include media content files such asmusic, movies, television programming, games, and advertisements.

User data server 114 may include hardware and software components thatstore and process data for multiple users relating to each user'sactivities and usage of the content distribution network 100. Forexample, the content management server 102 may record and track eachuser's system usage, including his or her user device 106, contentresources accessed, and interactions with other user devices 106. Thisdata may be stored and processed by the user data server 114, to supportuser tracking and analysis features. For instance, in the professionaltraining and educational contexts, the user data server 114 may storeand analyze each user's training materials viewed, presentationsattended, courses completed, interactions, evaluation results, and thelike. The user data server 114 may also include a repository foruser-generated material, such as evaluations and tests completed byusers, and documents and assignments prepared by users. In the contextof media distribution and interactive gaming, the user data server 114may store and process resource access data for multiple users (e.g.,content titles accessed, access times, data usage amounts, gaminghistories, user devices and device types, etc.).

Administrator server 116 may include hardware and software components toinitiate various administrative functions at the content managementserver 102 and other components within the content distribution network100. For example, the administrator server 116 may monitor device statusand performance for the various servers, data stores, and/or userdevices 106 in the content distribution network 100. When necessary, theadministrator server 116 may add or remove devices from the network 100,and perform device maintenance such as providing software updates to thedevices in the network 100. Various administrative tools on theadministrator server 116 may allow authorized users to set user accesspermissions to various content resources, monitor resource usage byusers and devices 106, and perform analyses and generate reports onspecific network users and/or devices (e.g., resource usage trackingreports, training evaluations, etc.).

The content distribution network 100 may include one or morecommunication networks 120. Although only a single network 120 isidentified in FIG. 1, the content distribution network 100 may includeany number of different communication networks between any of thecomputer servers and devices shown in FIG. 1 and/or other devicesdescribed herein. Communication networks 120 may enable communicationbetween the various computing devices, servers, and other components ofthe content distribution network 100. As discussed below, variousimplementations of content distribution networks 100 may employdifferent types of networks 120, for example, computer networks,telecommunications networks, wireless networks, and/or any combinationof these and/or other networks.

The content distribution network 100 may include one or severalnavigation systems or features including, for example, the GlobalPositioning System (“GPS”), GALILEO, or the like, or location systems orfeatures including, for example, one or several transceivers that candetermine location of the one or several components of the contentdistribution network 100 via, for example, triangulation. All of theseare depicted as navigation system 122.

In some embodiments, navigation system 122 can include one or severalfeatures that can communicate with one or several components of thecontent distribution network 100 including, for example, with one orseveral of the user devices 106 and/or with one or several of thesupervisor devices 110. In some embodiments, this communication caninclude the transmission of a signal from the navigation system 122which signal is received by one or several components of the contentdistribution network 100 and can be used to determine the location ofthe one or several components of the content distribution network 100.

With reference to FIG. 2, an illustrative distributed computingenvironment 200 is shown including a computer server 202, four clientcomputing devices 206, and other components that may implement certainembodiments and features described herein. In some embodiments, theserver 202 may correspond to the content management server 102 discussedabove in FIG. 1, and the client computing devices 206 may correspond tothe user devices 106. However, the computing environment 200 illustratedin FIG. 2 may correspond to any other combination of devices and serversconfigured to implement a client-server model or other distributedcomputing architecture.

Client devices 206 may be configured to receive and execute clientapplications over one or more networks 220. Such client applications maybe web browser-based applications and/or standalone softwareapplications, such as mobile device applications. Server 202 may becommunicatively coupled with the client devices 206 via one or morecommunication networks 220. Client devices 206 may receive clientapplications from server 202 or from other application providers (e.g.,public or private application stores). Server 202 may be configured torun one or more server software applications or services, for example,web-based or cloud-based services, to support content distribution andinteraction with client devices 206. Users operating client devices 206may in turn utilize one or more client applications (e.g., virtualclient applications) to interact with server 202 to utilize the servicesprovided by these components.

Various different subsystems and/or components 204 may be implemented onserver 202. Users operating the client devices 206 may initiate one ormore client applications to use services provided by these subsystemsand components. The subsystems and components within the server 202 andclient devices 206 may be implemented in hardware, firmware, software,or combinations thereof. Various different system configurations arepossible in different distributed computing systems 200 and contentdistribution networks 100. The embodiment shown in FIG. 2 is thus oneexample of a distributed computing system and is not intended to belimiting.

Although exemplary computing environment 200 is shown with four clientcomputing devices 206, any number of client computing devices may besupported. Other devices, such as specialized sensor devices, etc., mayinteract with client devices 206 and/or server 202.

As shown in FIG. 2, various security and integration components 208 maybe used to send and manage communications between the server 202 anduser devices 206 over one or more communication networks 220. Thesecurity and integration components 208 may include separate servers,such as web servers and/or authentication servers, and/or specializednetworking components, such as firewalls, routers, gateways, loadbalancers, and the like. In some cases, the security and integrationcomponents 208 may correspond to a set of dedicated hardware and/orsoftware operating at the same physical location and under the controlof same entities as server 202. For example, components 208 may includeone or more dedicated web servers and network hardware in a datacenteror a cloud infrastructure. In other examples, the security andintegration components 208 may correspond to separate hardware andsoftware components which may be operated at a separate physicallocation and/or by a separate entity.

Security and integration components 208 may implement various securityfeatures for data transmission and storage, such as authenticating usersand restricting access to unknown or unauthorized users. In variousimplementations, security and integration components 208 may provide,for example, a file-based integration scheme or a service-basedintegration scheme for transmitting data between the various devices inthe content distribution network 100. Security and integrationcomponents 208 also may use secure data transmission protocols and/orencryption for data transfers, for example, File Transfer Protocol(FTP), Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy(PGP) encryption.

In some embodiments, one or more web services may be implemented withinthe security and integration components 208 and/or elsewhere within thecontent distribution network 100. Such web services, includingcross-domain and/or cross-platform web services, may be developed forenterprise use in accordance with various web service standards, such asRESTful web services (i.e., services based on the Representation StateTransfer (REST) architectural style and constraints), and/or webservices designed in accordance with the Web Service Interoperability(WS-I) guidelines. Some web services may use the Secure Sockets Layer(SSL) or Transport Layer Security (TLS) protocol to provide secureconnections between the server 202 and user devices 206. SSL or TLS mayuse HTTP or HTTPS to provide authentication and confidentiality. Inother examples, web services may be implemented using REST over HTTPSwith the OAuth open standard for authentication, or using theWS-Security standard which provides for secure SOAP messages using XMLencryption. In other examples, the security and integration components208 may include specialized hardware for providing secure web services.For example, security and integration components 208 may include securenetwork appliances having built-in features such as hardware-acceleratedSSL and HTTPS, WS-Security, and firewalls. Such specialized hardware maybe installed and configured in front of any web servers, so that anyexternal devices may communicate directly with the specialized hardware.

Communication network(s) 220 may be any type of network familiar tothose skilled in the art that can support data communications using anyof a variety of commercially-available protocols, including withoutlimitation, TCP/IP (transmission control protocol/Internet protocol),SNA (systems network architecture), IPX (Internet packet exchange),Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols,Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text TransferProtocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and thelike. Merely by way of example, network(s) 220 may be local areanetworks (LAN), such as one based on Ethernet, Token-Ring and/or thelike. Network(s) 220 also may be wide-area networks, such as theInternet. Networks 220 may include telecommunication networks such aspublic switched telephone networks (PSTNs), or virtual networks such asan intranet or an extranet. Infrared and wireless networks (e.g., usingthe Institute of Electrical and Electronics (IEEE) 802.11 protocol suiteor other wireless protocols) also may be included in networks 220.

Computing environment 200 also may include one or more data stores 210and/or back-end servers 212. In certain examples, the data stores 210may correspond to data store server(s) 104 discussed above in FIG. 1,and back-end servers 212 may correspond to the various back-end servers112-116. Data stores 210 and servers 212 may reside in the samedatacenter or may operate at a remote location from server 202. In somecases, one or more data stores 210 may reside on a non-transitorystorage medium within the server 202. Other data stores 210 and back-endservers 212 may be remote from server 202 and configured to communicatewith server 202 via one or more networks 220. In certain embodiments,data stores 210 and back-end servers 212 may reside in a storage-areanetwork (SAN), or may use storage-as-a-service (STaaS) architecturalmodel.

With reference to FIG. 3, an illustrative set of data stores and/or datastore servers is shown, corresponding to the data store servers 104 ofthe content distribution network 100 discussed above in FIG. 1. One ormore individual data stores 301-311 may reside in storage on a singlecomputer server 104 (or a single server farm or cluster) under thecontrol of a single entity, or may reside on separate servers operatedby different entities and/or at remote locations. In some embodiments,data stores 301-311 may be accessed by the content management server 102and/or other devices and servers within the network 100 (e.g., userdevices 106, supervisor devices 110, administrator servers 116, etc.).Access to one or more of the data stores 301-311 may be limited ordenied based on the processes, user credentials, and/or devicesattempting to interact with the data store.

The paragraphs below describe examples of specific data stores that maybe implemented within some embodiments of a content distribution network100. It should be understood that the below descriptions of data stores301-311, including their functionality and types of data stored therein,are illustrative and non-limiting. Data stores server architecture,design, and the execution of specific data stores 301-311 may depend onthe context, size, and functional requirements of a content distributionnetwork 100. For example, in content distribution systems 100 used forprofessional training and educational purposes, separate databases orfile-based storage systems may be implemented in data store server(s)104 to store trainee and/or student data, trainer and/or professor data,training module data and content descriptions, training results,evaluation data, and the like. In contrast, in content distributionsystems 100 used for media distribution from content providers tosubscribers, separate data stores may be implemented in data storesserver(s) 104 to store listings of available content titles anddescriptions, content title usage statistics, subscriber profiles,account data, payment data, network usage statistics, etc.

A user profile data store 301, also referred to herein as a user profiledatabase 301, may include information relating to the end users withinthe content distribution network 100. This information may include usercharacteristics such as the user names, access credentials (e.g., loginsand passwords), user preferences, and information relating to anyprevious user interactions within the content distribution network 100(e.g., requested content, posted content, content modules completed,training scores or evaluations, other associated users, etc.). In someembodiments, this information can relate to one or several individualend users such as, for example, one or several students, teachers,administrators, or the like, and in some embodiments, this informationcan relate to one or several institutional end users such as, forexample, one or several schools, groups of schools such as one orseveral school districts, one or several colleges, one or severaluniversities, one or several training providers, or the like. In someembodiments, this information can identify one or several usermemberships in one or several groups such as, for example, a student'smembership in a university, school, program, grade, course, class, orthe like.

The user profile database 301 can include information relating to auser's status, location, or the like. This information can identify, forexample, a device a user is using, the location of that device, or thelike. In some embodiments, this information can be generated based onany location detection technology including, for example, a navigationsystem 122, or the like.

Information relating to the user's status can identify, for example,logged-in status information that can indicate whether the user ispresently logged-in to the content distribution network 100 and/orwhether the log-in-is active. In some embodiments, the informationrelating to the user's status can identify whether the user is currentlyaccessing content and/or participating in an activity from the contentdistribution network 100.

In some embodiments, information relating to the user's status canidentify, for example, one or several attributes of the user'sinteraction with the content distribution network 100, and/or contentdistributed by the content distribution network 100. This can includedata identifying the user's interactions with the content distributionnetwork 100, the content consumed by the user through the contentdistribution network 100, or the like. In some embodiments, this caninclude data identifying the type of information accessed through thecontent distribution network 100 and/or the type of activity performedby the user via the content distribution network 100, the lapsed timesince the last time the user accessed content and/or participated in anactivity from the content distribution network 100, or the like. In someembodiments, this information can relate to a content program comprisingan aggregate of data, content, and/or activities, and can identify, forexample, progress through the content program, or through the aggregateof data, content, and/or activities forming the content program. In someembodiments, this information can track, for example, the amount of timesince participation in and/or completion of one or several types ofactivities, the amount of time since communication with one or severalsupervisors and/or supervisor devices 110, or the like.

In some embodiments in which the one or several end users areindividuals, and specifically are students, the user profile database301 can further include information relating to these students' academicand/or educational history. This information can identify one or severalcourses of study that the student has initiated, completed, and/orpartially completed, as well as grades received in those courses ofstudy. In some embodiments, the student's academic and/or educationalhistory can further include information identifying student performanceon one or several tests, quizzes, and/or assignments. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

The user profile database 301 can include information relating to one orseveral student learning preferences. In some embodiments, for example,the user, also referred to herein as the student or the student-user mayhave one or several preferred learning styles, one or several mosteffective learning styles, and/or the like. In some embodiments, thestudent's learning style can be any learning style describing how thestudent best learns or how the student prefers to learn. In oneembodiment, these learning styles can include, for example,identification of the student as an auditory learner, as a visuallearner, and/or as a tactile learner. In some embodiments, the dataidentifying one or several student learning styles can include dataidentifying a learning style based on the student's educational historysuch as, for example, identifying a student as an auditory learner whenthe student has received significantly higher grades and/or scores onassignments and/or in courses favorable to auditory learners. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

In some embodiments, the user profile data store 301 can further includeinformation identifying one or several user skill levels. In someembodiments, these one or several user skill levels can identify a skilllevel determined based on past performance by the user interacting withthe content delivery network 100, and in some embodiments, these one orseveral user skill levels can identify a predicted skill leveldetermined based on past performance by the user interacting with thecontent delivery network 100 and one or several predictive models.

The user profile database 301 can further include information relatingto one or several teachers and/or instructors who are responsible fororganizing, presenting, and/or managing the presentation of informationto the student. In some embodiments, user profile database 301 caninclude information identifying courses and/or subjects that have beentaught by the teacher, data identifying courses and/or subjectscurrently taught by the teacher, and/or data identifying courses and/orsubjects that will be taught by the teacher. In some embodiments, thiscan include information relating to one or several teaching styles ofone or several teachers. In some embodiments, the user profile database301 can further include information indicating past evaluations and/orevaluation reports received by the teacher. In some embodiments, theuser profile database 301 can further include information relating toimprovement suggestions received by the teacher, training received bythe teacher, continuing education received by the teacher, and/or thelike. In some embodiments, this information can be stored in a tier ofmemory that is not the fastest memory in the content delivery network100.

An accounts data store 302, also referred to herein as an accountsdatabase 302, may generate and store account data for different users invarious roles within the content distribution network 100. For example,accounts may be created in an accounts data store 302 for individual endusers, supervisors, administrator users, and entities such as companiesor educational institutions. Account data may include account types,current account status, account characteristics, and any parameters,limits, restrictions associated with the accounts.

A content library data store 303, also referred to herein as a contentlibrary database 303, may include information describing the individualcontent items (or content resources or data packets) available via thecontent distribution network 100. In some embodiments, these datapackets in the content library database 303 can be linked to form anobject network. In some embodiments, these data packets can be linked inthe object network according to one or several sequential relationshipwhich can be, in some embodiments, prerequisite relationships that can,for example, identify the relative hierarchy and/or difficulty of thedata objects. In some embodiments, this hierarchy of data objects can begenerated by the content distribution network 100 according to userexperience with the object network, and in some embodiments, thishierarchy of data objects can be generated based on one or severalexisting and/or external hierarchies such as, for example, a syllabus, atable of contents, or the like. In some embodiments, for example, theobject network can correspond to a syllabus such that content for thesyllabus is embodied in the object network.

In some embodiments, the content library data store 303 can comprise asyllabus, a schedule, or the like. In some embodiments, the syllabus orschedule can identify one or several tasks and/or events relevant to theuser. In some embodiments, for example, when the user is a member of agroup such as a section or a class, these tasks and/or events relevantto the user can identify one or several assignments, quizzes, exams, orthe like.

In some embodiments, the library data store 303 may include metadata,properties, and other characteristics associated with the contentresources stored in the content server 112. Such data may identify oneor more aspects or content attributes of the associated contentresources, for example, subject matter, access level, or skill level ofthe content resources, license attributes of the content resources(e.g., any limitations and/or restrictions on the licensable use and/ordistribution of the content resource), price attributes of the contentresources (e.g., a price and/or price structure for determining apayment amount for use or distribution of the content resource), ratingattributes for the content resources (e.g., data indicating theevaluation or effectiveness of the content resource), and the like. Insome embodiments, the library data store 303 may be configured to allowupdating of content metadata or properties, and to allow the additionand/or removal of information relating to the content resources. Forexample, content relationships may be implemented as graph structures,which may be stored in the library data store 303 or in an additionalstore for use by selection algorithms along with the other metadata.

In some embodiments, the content library data store 303 can containinformation used in evaluating responses received from users. In someembodiments, for example, a user can receive content from the contentdistribution network 100 and can, subsequent to receiving that content,provide a response to the received content. In some embodiments, forexample, the received content can comprise one or several questions,prompts, or the like, and the response to the received content cancomprise an answer to those one or several questions, prompts, or thelike. In some embodiments, information, referred to herein as“comparative data,” from the content library data store 303 can be usedto determine whether the responses are the correct and/or desiredresponses.

In some embodiments, the content library database 303 and/or the userprofile database 301 can comprise an aggregation network, also referredto herein as a content network or content aggregation network. Theaggregation network can comprise a plurality of content aggregationsthat can be linked together by, for example: creation by common user;relation to a common subject, topic, skill, or the like; creation from acommon set of source material such as source data packets; or the like.In some embodiments, the content aggregation can comprise a grouping ofcontent comprising the presentation portion that can be provided to theuser in the form of, for example, a flash card and an extraction portionthat can comprise the desired response to the presentation portion suchas for example, an answer to a flash card. In some embodiments, one orseveral content aggregations can be generated by the contentdistribution network 100 and can be related to one or several datapackets that can be, for example, organized in object network. In someembodiments, the one or several content aggregations can be each createdfrom content stored in one or several of the data packets.

In some embodiments, the content aggregations located in the contentlibrary database 303 and/or the user profile database 301 can beassociated with a user-creator of those content aggregations. In someembodiments, access to content aggregations can vary based on, forexample, whether a user created the content aggregations. In someembodiments, the content library database 303 and/or the user profiledatabase 301 can comprise a database of content aggregations associatedwith a specific user, and in some embodiments, the content librarydatabase 303 and/or the user profile database 301 can comprise aplurality of databases of content aggregations that are each associatedwith a specific user. In some embodiments, these databases of contentaggregations can include content aggregations created by their specificuser and, in some embodiments, these databases of content aggregationscan further include content aggregations selected for inclusion by theirspecific user and/or a supervisor of that specific user. In someembodiments, these content aggregations can be arranged and/or linked ina hierarchical relationship similar to the data packets in the objectnetwork and/or linked to the object network in the object network or thetasks or skills associated with the data packets in the object networkor the syllabus or schedule.

In some embodiments, the content aggregation network, and the contentaggregations forming the content aggregation network can be organizedaccording to the object network and/or the hierarchical relationshipsembodied in the object network. In some embodiments, the contentaggregation network, and/or the content aggregations forming the contentaggregation network can be organized according to one or several tasksidentified in the syllabus, schedule or the like.

A pricing data store 304 may include pricing information and/or pricingstructures for determining payment amounts for providing access to thecontent distribution network 100 and/or the individual content resourceswithin the network 100. In some cases, pricing may be determined basedon a user's access to the content distribution network 100, for example,a time-based subscription fee, or pricing based on network usage. Inother cases, pricing may be tied to specific content resources. Certaincontent resources may have associated pricing information, whereas otherpricing determinations may be based on the resources accessed, theprofiles and/or accounts of the user, and the desired level of access(e.g., duration of access, network speed, etc.). Additionally, thepricing data store 304 may include information relating to compilationpricing for groups of content resources, such as group prices and/orprice structures for groupings of resources.

A license data store 305 may include information relating to licensesand/or licensing of the content resources within the contentdistribution network 100. For example, the license data store 305 mayidentify licenses and licensing terms for individual content resourcesand/or compilations of content resources in the content server 112, therights holders for the content resources, and/or common or large-scaleright holder information such as contact information for rights holdersof content not included in the content server 112.

A content access data store 306 may include access rights and securityinformation for the content distribution network 100 and specificcontent resources. For example, the content access data store 306 mayinclude login information (e.g., user identifiers, logins, passwords,etc.) that can be verified during user login attempts to the network100. The content access data store 306 also may be used to storeassigned user roles and/or user levels of access. For example, a user'saccess level may correspond to the sets of content resources and/or theclient or server applications that the user is permitted to access.Certain users may be permitted or denied access to certain applicationsand resources based on their subscription level, training program,course/grade level, etc. Certain users may have supervisory access overone or more end users, allowing the supervisor to access all or portionsof the end user's content, activities, evaluations, etc. Additionally,certain users may have administrative access over some users and/or someapplications in the content management network 100, allowing such usersto add and remove user accounts, modify user access permissions, performmaintenance updates on software and servers, etc.

A source data store 307 may include information relating to the sourceof the content resources available via the content distribution network.For example, a source data store 307 may identify the authors andoriginating devices of content resources, previous pieces of data and/orgroups of data originating from the same authors or originating devices,and the like.

An evaluation data store 308 may include information used to direct theevaluation of users and content resources in the content managementnetwork 100. In some embodiments, the evaluation data store 308 maycontain, for example, the analysis criteria and the analysis guidelinesfor evaluating users (e.g., trainees/students, gaming users, mediacontent consumers, etc.) and/or for evaluating the content resources inthe network 100. The evaluation data store 308 also may includeinformation relating to evaluation processing tasks, for example, theidentification of users and user devices 106 that have received certaincontent resources or accessed certain applications, the status ofevaluations or evaluation histories for content resources, users, orapplications, and the like. Evaluation criteria may be stored in theevaluation data store 308 including data and/or instructions in the formof one or several electronic rubrics or scoring guides for use in theevaluation of the content, users, or applications. The evaluation datastore 308 also may include past evaluations and/or evaluation analysesfor users, content, and applications, including relative rankings,characterizations, explanations, and the like.

A model data store 309, also referred to herein as a model database 309,can store information relating to one or several predictive models whichpredictive models can be, for example, statistical models. In someembodiments, these can include one or several evidence models, riskmodels, skill models, or the like. In some embodiments, an evidencemodel can be a mathematically-based statistical model. The evidencemodel can be based on, for example, Item Response Theory (IRT), BayesianNetwork (Bayes net), Performance Factor Analysis (PFA), or the like. Theevidence model can, in some embodiments, be customizable to a userand/or to one or several content items. Specifically, one or severalinputs relating to the user and/or to one or several content items canbe inserted into the evidence model. These inputs can include, forexample, one or several measures of user skill level, one or severalmeasures of content item difficulty and/or skill level, or the like. Thecustomized evidence model can then be used to predict the likelihood ofthe user providing desired or undesired responses to one or several ofthe content items.

In some embodiments, the risk models can include one or several modelsthat can be used to calculate one or several model function values. Insome embodiments, these one or several model function values can be usedto calculate a risk probability, which risk probability can characterizethe risk of a user such as a student-user failing to achieve a desiredoutcome such as, for example, failing to correctly respond to one orseveral data packets, failure to achieve a desired level of completionof a program, for example in a pre-defined time period, failure toachieve a desired learning outcome, or the like. In some embodiments,the risk probability can identify the risk of the student-user failingto complete 60% of the program.

In some embodiments, these models can include a plurality of modelfunctions including, for example, a first model function, a second modelfunction, a third model function, and a fourth model function. In someembodiments, some or all of the model functions can be associated with aportion of the program such as, for example, a completion stage and/orcompletion status of the program. In one embodiment, for example, thefirst model function can be associated with a first completion status,the second model function can be associated with a second completionstatus, the third model function can be associated with a thirdcompletion status, and the fourth model function can be associated witha fourth completion status. In some embodiments, these completionstatuses can be selected such that some or all of these completionstatuses are less than the desired level of completion of the program.Specifically, in some embodiments, these completion statuses can beselected to all be at less than 60% completion of the program, and morespecifically, in some embodiments, the first completion status can be at20% completion of the program, the second completion status can be at30% completion of the program, the third completion status can be at 40%completion of the program, and the fourth completion status can be at50% completion of the program. Similarly, any desired number of modelfunctions can be associated with any desired number of completionstatuses.

In some embodiments, a model function can be selected from the pluralityof model functions based on a student-user's progress through a program.In some embodiments, the student-user's progress can be compared to oneor several status trigger thresholds, each of which status triggerthresholds can be associated with one or more of the model functions. Ifone of the status triggers is triggered by the student-user's progress,the corresponding one or several model functions can be selected.

The model functions can comprise a variety of types of models and/orfunctions. In some embodiments, each of the model functions outputs afunction value that can be used in calculating a risk probability. Thisfunction value can be calculated by performing one or severalmathematical operations on one or several values indicative of one orseveral user attributes and/or user parameters, also referred to hereinas program status parameters. In some embodiments, each of the modelfunctions can use the same program status parameters, and in someembodiments, the model functions can use different program statusparameters. In some embodiments, the model functions use differentprogram status parameters when at least one of the model functions usesat least one program status parameter that is not used by others of themodel functions.

In some embodiments, a skill model can comprise a statistical modelidentifying a predictive skill level of one or several students. In someembodiments, this model can identify a single skill level of a studentand/or a range of possible skill levels of a student. In someembodiments, this statistical model can identify a skill level of astudent-user and an error value or error range associated with thatskill level. In some embodiments, the error value can be associated witha confidence interval determined based on a confidence level. Thus, insome embodiments, as the number of student interactions with the contentdistribution network increases, the confidence level can increase andthe error value can decrease such that the range identified by the errorvalue about the predicted skill level is smaller.

A threshold database 310, also referred to herein as a thresholddatabase, can store one or several threshold values. These one orseveral threshold values can delineate between states or conditions. Inone exemplary embodiment, for example, a threshold value can delineatebetween an acceptable user performance and an unacceptable userperformance, between content appropriate for a user and content that isinappropriate for a user, between risk levels, or the like.

In addition to the illustrative data stores described above, data storeserver(s) 104 (e.g., database servers, file-based storage servers, etc.)may include one or more external data aggregators 311. External dataaggregators 311 may include third-party data sources accessible to thecontent management network 100, but not maintained by the contentmanagement network 100. External data aggregators 311 may include anyelectronic information source relating to the users, content resources,or applications of the content distribution network 100. For example,external data aggregators 311 may be third-party data stores containingdemographic data, education-related data, consumer sales data,health-related data, and the like. Illustrative external dataaggregators 311 may include, for example, social networking web servers,public records data stores, learning management systems, educationalinstitution servers, business servers, consumer sales data stores,medical record data stores, etc. Data retrieved from various externaldata aggregators 311 may be used to verify and update user accountinformation, suggest user content, and perform user and contentevaluations.

With reference now to FIG. 4, a block diagram is shown illustrating anembodiment of one or more content management servers 102 within acontent distribution network 100. In such an embodiment, contentmanagement server 102 performs internal data gathering and processing ofstreamed content along with external data gathering and processing.Other embodiments could have either all external or all internal datagathering. This embodiment allows reporting timely information thatmight be of interest to the reporting party or other parties. In thisembodiment, the content management server 102 can monitor gatheredinformation from several sources to allow it to make timely businessand/or processing decisions based upon that information. For example,reports of user actions and/or responses, as well as the status and/orresults of one or several processing tasks could be gathered andreported to the content management server 102 from a number of sources.

Internally, the content management server 102 gathers information fromone or more internal components 402-408. The internal components 402-408gather and/or process information relating to such things as: contentprovided to users; content consumed by users; responses provided byusers; user skill levels; content difficulty levels; next content forproviding to users; etc. The internal components 402-408 can report thegathered and/or generated information in real-time, near real-time oralong another time line. To account for any delay in reportinginformation, a time stamp or staleness indicator can inform others ofhow timely the information was sampled. The content management server102 can opt to allow third parties to use internally or externallygathered information that is aggregated within the server 102 bysubscription to the content distribution network 100.

A command and control (CC) interface 338 configures the gathered inputinformation to an output of data streams, also referred to herein ascontent streams. APIs for accepting gathered information and providingdata streams are provided to third parties external to the server 102who want to subscribe to data streams. The server 102 or a third partycan design as yet undefined APIs using the CC interface 338. The server102 can also define authorization and authentication parameters usingthe CC interface 338 such as authentication, authorization, login,and/or data encryption. CC information is passed to the internalcomponents 402-408 and/or other components of the content distributionnetwork 100 through a channel separate from the gathered information ordata stream in this embodiment, but other embodiments could embed CCinformation in these communication channels. The CC information allowsthrottling information reporting frequency, specifying formats forinformation and data streams, deactivation of one or several internalcomponents 402-408 and/or other components of the content distributionnetwork 100, updating authentication and authorization, etc.

The various data streams that are available can be researched andexplored through the CC interface 338. Those data stream selections fora particular subscriber, which can be one or several of the internalcomponents 402-408 and/or other components of the content distributionnetwork 100, are stored in the queue subscription information database322. The server 102 and/or the CC interface 338 then routes selecteddata streams to processing subscribers that have selected delivery of agiven data stream. Additionally, the server 102 also supports historicalqueries of the various data streams that are stored in an historicaldata store 334 as gathered by an archive data agent 336. Through the CCinterface 238 various data streams can be selected for archiving intothe historical data store 334.

Components of the content distribution network 100 outside of the server102 can also gather information that is reported to the server 102 inreal-time, near real-time or along another time line. There is a definedAPI between those components and the server 102. Each type ofinformation or variable collected by server 102 falls within a definedAPI or multiple APIs. In some cases, the CC interface 338 is used todefine additional variables to modify an API that might be of use toprocessing subscribers. The additional variables can be passed to allprocessing subscribes or just a subset. For example, a component of thecontent distribution network 100 outside of the server 102 may report auser response but define an identifier of that user as a privatevariable that would not be passed to processing subscribers lackingaccess to that user and/or authorization to receive that user data.Processing subscribers having access to that user and/or authorizationto receive that user data would receive the subscriber identifier alongwith response reported that component. Encryption and/or uniqueaddressing of data streams or sub-streams can be used to hide theprivate variables within the messaging queues.

The user devices 106 and/or supervisor devices 110 communicate with theserver 102 through security and/or integration hardware 410. Thecommunication with security and/or integration hardware 410 can beencrypted or not. For example, a socket using a TCP connection could beused. In addition to TCP, other transport layer protocols like SCTP andUDP could be used in some embodiments to intake the gatheredinformation. A protocol such as SSL could be used to protect theinformation over the TCP connection. Authentication and authorizationcan be performed to any user devices 106 and/or supervisor deviceinterfacing to the server 102. The security and/or integration hardware410 receives the information from one or several of the user devices 106and/or the supervisor devices 110 by providing the API and anyencryption, authorization, and/or authentication. In some cases, thesecurity and/or integration hardware 410 reformats or rearranges thisreceived information.

The messaging bus 412, also referred to herein as a messaging queue or amessaging channel, can receive information from the internal componentsof the server 102 and/or components of the content distribution network100 outside of the server 102 and distribute the gathered information asa data stream to any processing subscribers that have requested the datastream from the messaging queue 412. As indicate in FIG. 4, processingsubscribers are indicated by a connector to the messaging bus 412, theconnector having an arrow head pointing away from the messaging bus 412.Only data streams within the messaging queue 412 that a particularprocessing subscriber has subscribed to may be read by that processingsubscriber if received at all. Gathered information sent to themessaging queue 412 is processed and returned in a data stream in afraction of a second by the messaging queue 412. Various multicastingand routing techniques can be used to distribute a data stream from themessaging queue 412 that a number of processing subscribers haverequested. Protocols such as Multicast or multiple Unicast could be usedto distribute streams within the messaging queue 412. Additionally,transport layer protocols like TCP, SCTP and UDP could be used invarious embodiments.

Through the CC interface 338, an external or internal processingsubscriber can be assigned one or more data streams within the messagingqueue 412. A data stream is a particular type of message in a particularcategory. For example, a data stream can comprise all of the datareported to the messaging bus 412 by a designated set of components. Oneor more processing subscribers could subscribe and receive the datastream to process the information and make a decision and/or feed theoutput from the processing as gathered information fed back into themessaging queue 412. Through the CC interface 338 a developer can searchthe available data streams or specify a new data stream and its API. Thenew data stream might be determined by processing a number of existingdata streams with a processing subscriber.

The CDN 110 has internal processing subscribers 402-408 that processassigned data streams to perform functions within the server 102.Internal processing subscribers 402-408 could perform functions such asproviding content to a user, receiving a response from a user,determining the correctness of the received response, updating one orseveral models based on the correctness of the response, recommendingnew content for providing to one or several users, or the like. Theinternal processing subscribers 402-408 can decide filtering andweighting of records from the data stream. To the extent that decisionsare made based upon analysis of the data stream, each data record istime stamped to reflect when the information was gathered such thatadditional credibility could be given to more recent results, forexample. Other embodiments may filter out records in the data streamthat are from an unreliable source or stale. For example, a particularcontributor of information may prove to have less than optimal gatheredinformation and that could be weighted very low or removed altogether.

Internal processing subscribers 402-408 may additionally process one ormore data streams to provide different information to feed back into themessaging queue 412 to be part of a different data stream. For example,hundreds of user devices 106 could provide responses that are put into adata stream on the messaging queue 412. An internal processingsubscriber 402-408 could receive the data stream and process it todetermine the difficulty of one or several data packets provided to oneor several users, and supply this information back onto the messagingqueue 412 for possible use by other internal and external processingsubscribers.

As mentioned above, the CC interface 338 allows the CDN 110 to queryhistorical messaging queue 412 information. An archive data agent 336listens to the messaging queue 412 to store data streams in a historicaldatabase 334. The historical database 334 may store data streams forvarying amounts of time and may not store all data streams. Differentdata streams may be stored for different amounts of time.

With regard to the components 402-48, the content management server(s)102 may include various server hardware and software components thatmanage the content resources within the content distribution network 100and provide interactive and adaptive content to users on various userdevices 106. For example, content management server(s) 102 may provideinstructions to and receive information from the other devices withinthe content distribution network 100, in order to manage and transmitcontent resources, user data, and server or client applicationsexecuting within the network 100.

A content management server 102 may include a packet selection system402. The packet selection system 402 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a packetselection server 402), or using designated hardware and softwareresources within a shared content management server 102. In someembodiments, the packet selection system 402 may adjust the selectionand adaptive capabilities of content resources to match the needs anddesires of the users receiving the content. For example, the packetselection system 402 may query various data stores and servers 104 toretrieve user information, such as user preferences and characteristics(e.g., from a user profile data store 301), user access restrictions tocontent recourses (e.g., from a content access data store 306), previoususer results and content evaluations (e.g., from an evaluation datastore 308), and the like. Based on the retrieved information from datastores 104 and other data sources, the packet selection system 402 maymodify content resources for individual users.

In some embodiments, the packet selection system 402 can include arecommendation engine, also referred to herein as an adaptiverecommendation engine. In some embodiments, the recommendation enginecan select one or several pieces of content, also referred to herein asdata packets, for providing to a user. These data packets can beselected based on, for example, the information retrieved from thedatabase server 104 including, for example, the user profile database301, the content library database 303, the model database 309, or thelike. In some embodiments, these one or several data packets can beadaptively selected and/or selected according to one or severalselection rules. In one embodiment, for example, the recommendationengine can retrieve information from the user profile database 301identifying, for example, a skill level of the user. The recommendationengine can further retrieve information from the content librarydatabase 303 identifying, for example, potential data packets forproviding to the user and the difficulty of those data packets and/orthe skill level associated with those data packets.

The recommendation engine can identify one or several potential datapackets for providing and/or one or several data packets for providingto the user based on, for example, one or several rules, models,predictions, or the like. The recommendation engine can use the skilllevel of the user to generate a prediction of the likelihood of one orseveral users providing a desired response to some or all of thepotential data packets. In some embodiments, the recommendation enginecan pair one or several data packets with selection criteria that may beused to determine which packet should be delivered to a student-userbased on one or several received responses from that student-user. Insome embodiments, one or several data packets can be eliminated from thepool of potential data packets if the prediction indicates either toohigh a likelihood of a desired response or too low a likelihood of adesired response. In some embodiments, the recommendation engine canthen apply one or several selection criteria to the remaining potentialdata packets to select a data packet for providing to the user. Theseone or several selection criteria can be based on, for example, criteriarelating to a desired estimated time for receipt of response to the datapacket, one or several content parameters, one or several assignmentparameters, or the like.

A content management server 102 also may include a summary model system404. The summary model system 404 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a summarymodel server 404), or using designated hardware and software resourceswithin a shared content management server 102. In some embodiments, thesummary model system 404 may monitor the progress of users throughvarious types of content resources and groups, such as mediacompilations, courses or curriculums in training or educationalcontexts, interactive gaming environments, and the like. For example,the summary model system 404 may query one or more databases and/or datastore servers 104 to retrieve user data such as associated contentcompilations or programs, content completion status, user goals,results, and the like.

A content management server 102 also may include a response system 406,which can include, in some embodiments, a response processor. Theresponse system 406 may be implemented using dedicated hardware withinthe content distribution network 100 (e.g., a response server 406), orusing designated hardware and software resources within a shared contentmanagement server 102. The response system 406 may be configured toreceive and analyze information from user devices 106. For example,various ratings of content resources submitted by users may be compiledand analyzed, and then stored in a data store (e.g., a content librarydata store 303 and/or evaluation data store 308) associated with thecontent. In some embodiments, the response server 406 may analyze theinformation to determine the effectiveness or appropriateness of contentresources with, for example, a subject matter, an age group, a skilllevel, or the like. In some embodiments, the response system 406 mayprovide updates to the packet selection system 402 or the summary modelsystem 404, with the attributes of one or more content resources orgroups of resources within the network 100. The response system 406 alsomay receive and analyze user evaluation data from user devices 106,supervisor devices 110, and administrator servers 116, etc. Forinstance, response system 406 may receive, aggregate, and analyze userevaluation data for different types of users (e.g., end users,supervisors, administrators, etc.) in different contexts (e.g., mediaconsumer ratings, trainee or student comprehension levels, teachereffectiveness levels, gamer skill levels, etc.).

In some embodiments, the response system 406 can be further configuredto receive one or several responses from the user and analyze these oneor several responses. In some embodiments, for example, the responsesystem 406 can be configured to translate the one or several responsesinto one or several observables. As used herein, an observable is acharacterization of a received response. In some embodiments, thetranslation of the one or several responses into one or severalobservables can include determining whether the one or several responsesare correct responses, also referred to herein as desired responses, orare incorrect responses, also referred to herein as undesired responses.In some embodiments, the translation of the one or several responsesinto one or several observables can include characterizing the degree towhich one or several responses are desired responses and/or undesiredresponses. In some embodiments, one or several values can be generatedby the response system 406 to reflect user performance in responding tothe one or several data packets. In some embodiments, these one orseveral values can comprise one or several scores for one or severalresponses and/or data packets.

A content management server 102 also may include a presentation system408. The presentation system 408 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., apresentation server 408), or using designated hardware and softwareresources within a shared content management server 102. Thepresentation system 408 can include a presentation engine that can be,for example, a software module running on the content delivery system.

The presentation system 408, also referred to herein as the presentationmodule or the presentation engine, may receive content resources fromthe packet selection system 402 and/or from the summary model system404, and provide the resources to user devices 106. The presentationsystem 408 may determine the appropriate presentation format for thecontent resources based on the user characteristics and preferences,and/or the device capabilities of user devices 106. If needed, thepresentation system 408 may convert the content resources to theappropriate presentation format and/or compress the content beforetransmission. In some embodiments, the presentation system 408 may alsodetermine the appropriate transmission media and communication protocolsfor transmission of the content resources.

In some embodiments, the presentation system 408 may include specializedsecurity and integration hardware 410, along with corresponding softwarecomponents to implement the appropriate security features contenttransmission and storage, to provide the supported network and clientaccess models, and to support the performance and scalabilityrequirements of the network 100. The security and integration layer 410may include some or all of the security and integration components 208discussed above in FIG. 2, and may control the transmission of contentresources and other data, as well as the receipt of requests and contentinteractions, to and from the user devices 106, supervisor devices 110,administrative servers 116, and other devices in the network 100.

With reference now to FIG. 5, a block diagram of an illustrativecomputer system is shown. The system 500 may correspond to any of thecomputing devices or servers of the content distribution network 100described above, or any other computing devices described herein, andspecifically can include, for example, one or several of the userdevices 106, the supervisor device 110, and/or any of the servers 102,104, 108, 112, 114, 116. In this example, computer system 500 includesprocessing units 504 that communicate with a number of peripheralsubsystems via a bus subsystem 502. These peripheral subsystems include,for example, a storage subsystem 510, an I/O subsystem 526, and acommunications subsystem 532.

Bus subsystem 502 provides a mechanism for letting the variouscomponents and subsystems of computer system 500 communicate with eachother as intended. Although bus subsystem 502 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 502 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Sucharchitectures may include, for example, an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 504, which may be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 500. One or more processors,including single core and/or multicore processors, may be included inprocessing unit 504. As shown in the figure, processing unit 504 may beimplemented as one or more independent processing units 506 and/or 508with single or multicore processors and processor caches included ineach processing unit. In other embodiments, processing unit 504 may alsobe implemented as a quad-core processing unit or larger multicoredesigns (e.g., hexa-core processors, octo-core processors, ten-coreprocessors, or greater).

Processing unit 504 may execute a variety of software processes embodiedin program code, and may maintain multiple concurrently executingprograms or processes. At any given time, some or all of the programcode to be executed can be resident in processor(s) 504 and/or instorage subsystem 510. In some embodiments, computer system 500 mayinclude one or more specialized processors, such as digital signalprocessors (DSPs), outboard processors, graphics processors,application-specific processors, and/or the like.

I/O subsystem 526 may include device controllers 528 for one or moreuser interface input devices and/or user interface output devices 530.User interface input and output devices 530 may be integral with thecomputer system 500 (e.g., integrated audio/video systems, and/ortouchscreen displays), or may be separate peripheral devices which areattachable/detachable from the computer system 500. The I/O subsystem526 may provide one or several outputs to a user by converting one orseveral electrical signals to the user in perceptible and/orinterpretable form, and may receive one or several inputs from the userby generating one or several electrical signals based on one or severaluser-caused interactions with the I/O subsystem such as the depressingof a key or button, the moving of a mouse, the interaction with atouchscreen or trackpad, the interaction of a sound wave with amicrophone, or the like.

Input devices 530 may include a keyboard, pointing devices such as amouse or trackball, a touchpad or touch screen incorporated into adisplay, a scroll wheel, a click wheel, a dial, a button, a switch, akeypad, audio input devices with voice command recognition systems,microphones, and other types of input devices. Input devices 530 mayalso include three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader 3D scanners, 3Dprinters, laser rangefinders, and eye gaze tracking devices. Additionalinput devices 530 may include, for example, motion sensing and/orgesture recognition devices that enable users to control and interactwith an input device through a natural user interface using gestures andspoken commands, eye gesture recognition devices that detect eyeactivity from users and transform the eye gestures as input into aninput device, voice recognition sensing devices that enable users tointeract with voice recognition systems through voice commands, medicalimaging input devices, MIDI keyboards, digital musical instruments, andthe like.

Output devices 530 may include one or more display subsystems, indicatorlights, or non-visual displays such as audio output devices, etc.Display subsystems may include, for example, cathode ray tube (CRT)displays, flat-panel devices, such as those using a liquid crystaldisplay (LCD) or plasma display, light-emitting diode (LED) displays,projection devices, touch screens, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system500 to a user or other computer. For example, output devices 530 mayinclude, without limitation, a variety of display devices that visuallyconvey text, graphics and audio/video information such as monitors,printers, speakers, headphones, automotive navigation systems, plotters,voice output devices, and modems.

Computer system 500 may comprise one or more storage subsystems 510,comprising hardware and software components used for storing data andprogram instructions, such as system memory 518 and computer-readablestorage media 516. The system memory 518 and/or computer-readablestorage media 516 may store program instructions that are loadable andexecutable on processing units 504, as well as data generated during theexecution of these programs.

Depending on the configuration and type of computer system 500, systemmemory 318 may be stored in volatile memory (such as random accessmemory (RAM) 512) and/or in non-volatile storage drives 514 (such asread-only memory (ROM), flash memory, etc.). The RAM 512 may containdata and/or program modules that are immediately accessible to and/orpresently being operated and executed by processing units 504. In someimplementations, system memory 518 may include multiple different typesof memory, such as static random access memory (SRAM) or dynamic randomaccess memory (DRAM). In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 500, such as duringstart-up, may typically be stored in the non-volatile storage drives514. By way of example, and not limitation, system memory 518 mayinclude application programs 520, such as client applications, Webbrowsers, mid-tier applications, server applications, etc., program data522, and an operating system 524.

Storage subsystem 510 also may provide one or more tangiblecomputer-readable storage media 516 for storing the basic programmingand data constructs that provide the functionality of some embodiments.Software (programs, code modules, instructions) that, when executed by aprocessor, provide the functionality described herein may be stored instorage subsystem 510. These software modules or instructions may beexecuted by processing units 504. Storage subsystem 510 may also providea repository for storing data used in accordance with the presentinvention.

Storage subsystem 300 may also include a computer-readable storage mediareader that can further be connected to computer-readable storage media516. Together and, optionally, in combination with system memory 518,computer-readable storage media 516 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 516 containing program code, or portionsof program code, may include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computer system 500.

By way of example, computer-readable storage media 516 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 516 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 516 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 500.

Communications subsystem 532 may provide a communication interface fromcomputer system 500 and external computing devices via one or morecommunication networks, including local area networks (LANs), wide areanetworks (WANs) (e.g., the Internet), and various wirelesstelecommunications networks. As illustrated in FIG. 5, thecommunications subsystem 532 may include, for example, one or morenetwork interface controllers (NICs) 534, such as Ethernet cards,Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as wellas one or more wireless communications interfaces 536, such as wirelessnetwork interface controllers (WNICs), wireless network adapters, andthe like. As illustrated in FIG. 5, the communications subsystem 532 mayinclude, for example, one or more location determining features 538 suchas one or several navigation system features and/or receivers, and thelike. Additionally and/or alternatively, the communications subsystem532 may include one or more modems (telephone, satellite, cable, ISDN),synchronous or asynchronous digital subscriber line (DSL) units,FireWire® interfaces, USB® interfaces, and the like. Communicationssubsystem 536 also may include radio frequency (RF) transceivercomponents for accessing wireless voice and/or data networks (e.g.,using cellular telephone technology, advanced data network technology,such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi(IEEE 802.11 family standards, or other mobile communicationtechnologies, or any combination thereof), global positioning system(GPS) receiver components, and/or other components.

The various physical components of the communications subsystem 532 maybe detachable components coupled to the computer system 500 via acomputer network, a FireWire® bus, or the like, and/or may be physicallyintegrated onto a motherboard of the computer system 500. Communicationssubsystem 532 also may be implemented in whole or in part by software.

In some embodiments, communications subsystem 532 may also receive inputcommunication in the form of structured and/or unstructured data feeds,event streams, event updates, and the like, on behalf of one or moreusers who may use or access computer system 500. For example,communications subsystem 532 may be configured to receive data feeds inreal-time from users of social networks and/or other communicationservices, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources(e.g., data aggregators 311). Additionally, communications subsystem 532may be configured to receive data in the form of continuous datastreams, which may include event streams of real-time events and/orevent updates (e.g., sensor data applications, financial tickers,network performance measuring tools, clickstream analysis tools,automobile traffic monitoring, etc.). Communications subsystem 532 mayoutput such structured and/or unstructured data feeds, event streams,event updates, and the like to one or more data stores 104 that may bein communication with one or more streaming data source computerscoupled to computer system 500.

Due to the ever-changing nature of computers and networks, thedescription of computer system 500 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software, or acombination. Further, connection to other computing devices, such asnetwork input/output devices, may be employed. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

With reference now to FIG. 6, a block diagram illustrating oneembodiment of the communication network is shown. Specifically, FIG. 6depicts one hardware configuration in which messages are exchangedbetween a source hub 602 via the communication network 120 that caninclude one or several intermediate hubs 604. In some embodiments, thesource hub 602 can be any one or several components of the contentdistribution network generating and initiating the sending of a message,and the terminal hub 606 can be any one or several components of thecontent distribution network 100 receiving and not re-sending themessage. In some embodiments, for example, the source hub 602 can be oneor several of the user device 106, the supervisor device 110, and/or theserver 102, and the terminal hub 606 can likewise be one or several ofthe user device 106, the supervisor device 110, and/or the server 102.In some embodiments, the intermediate hubs 604 can include any computingdevice that receives the message and resends the message to a next node.

As seen in FIG. 6, in some embodiments, each of the hubs 602, 604, 606can be communicatingly connected with the data store 104. In such anembodiments, some or all of the hubs 602, 604, 606 can send informationto the data store 104 identifying a received message and/or any sent orresent message. This information can, in some embodiments, be used todetermine the completeness of any sent and/or received messages and/orto verify the accuracy and completeness of any message received by theterminal hub 606.

In some embodiments, the communication network 120 can be formed by theintermediate hubs 604. In some embodiments, the communication network120 can comprise a single intermediate hub 604, and in some embodiments,the communication network 120 can comprise a plurality of intermediatehubs. In one embodiment, for example, and as depicted in FIG. 6, thecommunication network 120 includes a first intermediate hub 604-A and asecond intermediate hub 604-B.

With reference now to FIG. 7, a block diagram illustrating oneembodiment of user device 106 and supervisor device 110 communication isshown. In some embodiments, for example, a user may have multipledevices that can connect with the content distribution network 100 tosend or receive information. In some embodiments, for example, a usermay have a personal device such as a mobile device, a Smartphone, atablet, a Smartwatch, a laptop, a PC, or the like. In some embodiments,the other device can be any computing device in addition to the personaldevice. This other device can include, for example, a laptop, a PC, aSmartphone, a tablet, a Smartwatch, or the like. In some embodiments,the other device differs from the personal device in that the personaldevice is registered as such within the content distribution network 100and the other device is not registered as a personal device within thecontent distribution network 100.

Specifically with respect to FIG. 7, the user device 106 can include apersonal user device 106-A and one or several other user devices 106-B.In some embodiments, one or both of the personal user device 106-A andthe one or several other user devices 106-B can be communicatinglyconnected to the content management server 102 and/or to the navigationsystem 122. Similarly, the supervisor device 110 can include a personalsupervisor device 110-A and one or several other supervisor devices110-B. In some embodiments, one or both of the personal supervisordevice 110-A and the one or several other supervisor devices 110-B canbe communicatingly connected to the content management server 102 and/orto the navigation system 122.

In some embodiments, the content distribution network can send one ormore alerts to one or more user devices 106 and/or one or moresupervisor devices 110 via, for example, the communication network 120.In some embodiments, the receipt of the alert can result in thelaunching of an application within the receiving device, and in someembodiments, the alert can include a link that, when selected, launchesthe application or navigates a web-browser of the device of the selectorof the link to a page or portal associated with the alert.

In some embodiments, for example, the providing of this alert caninclude the identification of one or several user devices 106 and/orstudent-user accounts associated with the student-user and/or one orseveral supervisor devices 110 and/or supervisor-user accountsassociated with the supervisor-user. After these one or several devices106, 110 and/or accounts have been identified, the providing of thisalert can include determining an active device of the devices 106, 110based on determining which of the devices 106, 110 and/or accounts areactively being used, and then providing the alert to that active device.

Specifically, if the user is actively using one of the devices 106, 110such as the other user device 106-B and the other supervisor device110-B, and/or accounts, the alert can be provided to the user via thatother device 106-B, 110-B and/or account that is actively being used. Ifthe user is not actively using an other device 106-B, 110-B and/oraccount, a personal device 106-A, 110-A device, such as a smart phone ortablet, can be identified and the alert can be provided to this personaldevice 106-A, 110-A. In some embodiments, the alert can include code todirect the default device to provide an indicator of the received alertsuch as, for example, an aural, tactile, or visual indicator of receiptof the alert.

In some embodiments, the recipient device 106, 110 of the alert canprovide an indication of receipt of the alert. In some embodiments, thepresentation of the alert can include the control of the I/O subsystem526 to, for example, provide an aural, tactile, and/or visual indicatorof the alert and/or of the receipt of the alert. In some embodiments,this can include controlling a screen of the supervisor device 110 todisplay the alert, data contained in alert and/or an indicator of thealert.

With reference now to FIG. 8, a schematic illustration of one embodimentof an application stack, and particularly of a stack 650 is shown. Insome embodiments, the content distribution network 100 can comprise aportion of the stack 650 that can include an infrastructure layer 652, aplatform layer 654, an applications layer 656, and a products layer 658.In some embodiments, the stack 650 can comprise some or all of thelayers, hardware, and/or software to provide one or several desiredfunctionalities and/or productions.

As depicted in FIG. 8, the infrastructure layer 652 can include one orseveral servers, communication networks, data stores, privacy servers,and the like. In some embodiments, the infrastructure layer can furtherinclude one or several user devices 106 and/or supervisor devices 110connected as part of the content distribution network.

The platform layer can include one or several platform softwareprograms, modules, and/or capabilities. These can include, for example,identification services, security services, and/or adaptive platformservices 660. In some embodiments, the identification services can, forexample, identify one or several users, components of the contentdistribution network 100, or the like. The security services can monitorthe content distribution network for one or several security threats,breaches, viruses, malware, or the like. The adaptive platform services660 can receive information from one or several components of thecontent distribution network 100 and can provide predictions, models,recommendations, or the like based on that received information. Thefunctionality of the adaptive platform services 660 will be discussed ingreater detail in FIGS. 9A-9C, below.

The applications layer 656 can include software or software modules uponor in which one or several product softwares or product software modulescan operate. In some embodiments, the applications layer 656 caninclude, for example, a management system, record system, or the like.In some embodiments, the management system can include, for example, aLearning Management System (LMS), a Content Management System (CMS), orthe like. The management system can be configured to control thedelivery of one or several resources to a user and/or to receive one orseveral responses from the user. In some embodiments, the records systemcan include, for example, a virtual gradebook, a virtual counselor, orthe like.

The products layer can include one or several software products and/orsoftware module products. These software products and/or software moduleproducts can provide one or several services and/or functionalities toone or several users of the software products and/or software moduleproducts.

With reference now to FIG. 9A-9C, schematic illustrations of embodimentsof communication and processing flow of modules within the contentdistribution network 100 are shown. In some embodiments, thecommunication and processing can be performed in portions of theplatform layer 654 and/or applications layer 656. FIG. 9A depicts afirst embodiment of such communications or processing that can be in theplatform layer 654 and/or applications layer 656 via the message channel412.

The platform layer 654 and/or applications layer 656 can include aplurality of modules that can be embodied in software or hardware. Insome embodiments, some or all of the modules can be embodied in hardwareand/or software at a single location, and in some embodiments, some orall of these modules can be embodied in hardware and/or software atmultiple locations. These modules can perform one or several processesincluding, for example, a presentation process 670, a response process676, a summary model process 680, and a packet selection process 684.

The presentation process 670 can, in some embodiments, include one orseveral methods and/or steps to deliver content to one or several userdevices 106 and/or supervisor devices 110. The presentation process 670can be performed by a presenter module 672 and a view module 674. Thepresenter module 672 can be a hardware or software module of the contentdistribution network 100, and specifically of the server 102. In someembodiments, the presenter module 672 can include one or severalportions, features, and/or functionalities that are located on theserver 102 and/or one or several portions, features, and/orfunctionalities that are located on the user device 106. In someembodiments, the presenter module 672 can be embodied in thepresentation system 408.

The presenter module 672 can control the providing of content to one orseveral user devices 106 and/or supervisor devices 110. Specifically,the presenter module 672 can control the generation of one or severalmessages to provide content to one or several desired user devices 106and/or supervisor devices 110. The presenter module 672 can furthercontrol the providing of these one or several messages to the desiredone or several desired user devices 106 and/or supervisor devices 110.Thus, in some embodiments, the presenter module 672 can control one orseveral features of the communications subsystem 532 to generate andsend one or several electrical signals comprising content to one orseveral user devices 106 and/or supervisor devices 110.

In some embodiments, the presenter module 672 can control and/or managea portion of the presentation functions of the presentation process 670,and can specifically manage an “outer loop” of presentation functions.As used herein, the outer loop refers to tasks relating to the trackingof a user's progress through all or a portion of a group of datapackets. In some embodiments, this can include the identification of oneor several completed data packets or nodes and/or the non-adaptiveselection of one or several next data packets or nodes according to, forexample, one or several fixed rules. Such non-adaptive selection doesnot rely on the use of predictive models, but rather on rulesidentifying next data packets based on data relating to the completionof one or several previously completed data packets or assessmentsand/or whether one or several previously completed data packets weresuccessfully completed.

In some embodiments, and due to the management of the outer loop ofpresentation functions including the non-adaptive selection of one orseveral next data packets, nodes, or tasks by the presenter module, thepresenter module can function as a recommendation engine referred toherein as a first recommendation engine or a rules-based recommendationengine. In some embodiments, the first recommendation engine can beconfigured to select a next node for a user based on one or all of: theuser's current location in the content network; potential next nodes;the user's history including the user's previous responses; and one orseveral guard conditions associated with the potential next nodes. Insome embodiments, a guard condition defines one or several prerequisitesfor entry into, or exit from, a node.

In some embodiments, the presenter module 672 can include a portionlocated on the server 102 and/or a portion located on the user device106. In some embodiments, the portion of the presenter module 672located on the server 102 can receive data packet information andprovide a subset of the received data packet information to the portionof the presenter module 672 located on the user device 106. In someembodiments, this segregation of functions and/or capabilities canprevent solution data from being located on the user device 106 and frombeing potentially accessible by the user of the user device 106.

In some embodiments, the portion of the presenter module 672 located onthe user device 106 can be further configured to receive the subset ofthe data packet information from the portion of the presenter module 672located on the server 102 and provide that subset of the data packetinformation to the view module 674. In some embodiments, the portion ofthe presenter module 672 located on the user device 106 can be furtherconfigured to receive a content request from the view module 674 and toprovide that content request to the portion of the presenter module 674located on the server 102.

The view module 674 can be a hardware or software module of some or allof the user devices 106 and/or supervisor devices 110 of the contentdistribution network 100. The view module 674 can receive one or severalelectrical signals and/or communications from the presenter module 672and can provide the content received in those one or several electricalsignals and/or communications to the user of the user device 106 and/orsupervisor device 110 via, for example, the I/O subsystem 526.

In some embodiments, the view module 674 can control and/or monitor an“inner loop” of presentation functions. As used herein, the inner looprefers to tasks relating to the tracking and/or management of a user'sprogress through a data packet. This can specifically relate to thetracking and/or management of a user's progression through one orseveral pieces of content, questions, assessments, and/or the like of adata packet. In some embodiments, this can further include the selectionof one or several next pieces of content, next questions, nextassessments, and/or the like of the data packet for presentation and/orproviding to the user of the user device 106.

In some embodiments, one or both of the presenter module 672 and theview module 674 can comprise one or several presentation engines. Insome embodiments, these one or several presentation engines can comprisedifferent capabilities and/or functions. In some embodiments, one of thepresentation engines can be configured to track the progress of a userthrough a single data packet, task, content item, or the like, and insome embodiments, one of the presentation engines can track the progressof a user through a series of data packets, tasks, content items, or thelike.

The response process 676 can comprise one or several methods and/orsteps to evaluate a response. In some embodiments, this can include, forexample, determining whether the response comprises a desired responseand/or an undesired response. In some embodiments, the response process676 can include one or several methods and/or steps to determine thecorrectness and/or incorrectness of one or several received responses.In some embodiments, this can include, for example, determining thecorrectness and/or incorrectness of a multiple choice response, atrue/false response, a short answer response, an essay response, or thelike. In some embodiments, the response processor can employ, forexample, natural language processing, semantic analysis, or the like indetermining the correctness or incorrectness of the received responses.

In some embodiments, the response process 676 can be performed by aresponse processor 678. The response processor 678 can be a hardware orsoftware module of the content distribution network 100, andspecifically of the server 102. In some embodiments, the responseprocessor 678 can be embodied in the response system 406. In someembodiments, the response processor 678 can be communicatingly connectedto one or more of the modules of the presentation process 760 such as,for example, the presenter module 672 and/or the view module 674. Insome embodiments, the response processor 678 can be communicatinglyconnected with, for example, the message channel 412 and/or othercomponents and/or modules of the content distribution network 100.

The summary model process 680 can comprise one or several methods and/orsteps to generate and/or update one or several models. In someembodiments, this can include, for example, implementing informationreceived either directly or indirectly from the response processor 678to update one or several models. In some embodiments, the summary modelprocess 680 can include the update of a model relating to one or severaluser attributes such as, for example, a user skill model, a userknowledge model, a learning style model, or the like. In someembodiments, the summary model process 680 can include the update of amodel relating to one or several content attributes including attributesrelating to a single content item and/or data packet and/or attributesrelating to a plurality of content items and/or data packets. In someembodiments, these models can relate to an attribute of the one orseveral data packets such as, for example, difficulty, discrimination,required time, or the like.

In some embodiments, the summary model process 680 can be performed bythe model engine 682. In some embodiments, the model engine 682 can be ahardware or software module of the content distribution network 100, andspecifically of the server 102. In some embodiments, the model engine682 can be embodied in the summary model system 404.

In some embodiments, the model engine 682 can be communicatinglyconnected to one or more of the modules of the presentation process 760such as, for example, the presenter module 672 and/or the view module674, can be connected to the response processor 678 and/or therecommendation. In some embodiment, the model engine 682 can becommunicatingly connected to the message channel 412 and/or othercomponents and/or modules of the content distribution network 100.

The packet selection process 684 can comprise one or several stepsand/or methods to identify and/or select a data packet for presentationto a user. In some embodiments, this data packet can comprise aplurality of data packets. In some embodiments, this data packet can beselected according to one or several models updated as part of thesummary model process 680. In some embodiments, this data packet can beselected according to one or several rules, probabilities, models, orthe like. In some embodiments, the one or several data packets can beselected by the combination of a plurality of models updated in thesummary model process 680 by the model engine 682. In some embodiments,these one or several data packets can be selected by a recommendationengine 686. The recommendation engine 686 can be a hardware or softwaremodule of the content distribution network 100, and specifically of theserver 102. In some embodiments, the recommendation engine 686 can beembodied in the packet selection system 402. In some embodiments, therecommendation engine 686 can be communicatingly connected to one ormore of the modules of the presentation process 670, the responseprocess 676, and/or the summary model process 680 either directly and/orindirectly via, for example, the message channel.

In some embodiments, and as depicted in FIG. 9A, a presenter module 672can receive a data packet for presentation to a user device 106. Thisdata packet can be received, either directly or indirectly from arecommendation engine 686. In some embodiments, for example, thepresenter module 672 can receive a data packet for providing to a userdevice 106 from the recommendation engine 686, and in some embodiments,the presenter module 672 can receive an identifier of a data packet forproviding to a user device 106 via a view module 674. This can bereceived from the recommendation engine 686 via a message channel 412.Specifically, in some embodiments, the recommendation engine 686 canprovide data to the message channel 412 indicating the identificationand/or selection of a data packet for providing to a user via a userdevice 106. In some embodiments, this data indicating the identificationand/or selection of the data packet can identify the data packet and/orcan identify the intended recipient of the data packet.

The message channel 412 can output this received data in the form of adata stream 690 which can be received by, for example, the presentermodule 672, the model engine 682, and/or the recommendation engine 686.In some embodiments, some or all of: the presenter module 672, the modelengine 682, and/or the recommendation engine 686 can be configured toparse and/or filter the data stream 690 to identify data and/or eventsrelevant to their operation. Thus, for example, the presenter module 672can be configured to parse the data stream for information and/or eventsrelevant to the operation of the presenter module 672.

In some embodiments, the presenter module 672 can extract the datapacket from the data stream 690 and/or extract data identifying the datapacket and/or indicating the selecting of a data packet from the datastream. In the event that data identifying the data packet is extractedfrom the data stream 690, the presenter module 672 can request andreceive the data packet from the database server 104, and specificallyfrom the content library database 303. In embodiments in which dataindicating the selection of a data packet is extracted from the datastream 690, the presenter module 672 can request and receiveidentification of the data packet from the recommendation engine 686 andthen request and receive the data packet from the database server 104,and specifically from the content library database 303, and in someembodiments in which data indicating the selection of a data packet isextracted from the data stream 690, the presenter module 672 can requestand receive the data packet from the recommendation engine 686.

The presenter module can then provide the data packet and/or portions ofthe data packet to the view module 674. In some embodiments, forexample, the presenter module 672 can retrieve one or several rulesand/or conditions that can be, for example, associated with the datapacket and/or stored in the database server 104. In some embodiments,these rules and/or conditions can identify portions of a data packet forproviding to the view module 674 and/or portions of a data packet to notprovide to the view module 674. In some embodiments, for example,sensitive portions of a data packet, such as, for example, solutioninformation to any questions associated with a data packet, is notprovided to the view module 674 to prevent the possibility of undesiredaccess to those sensitive portions of the data packet. Thus, in someembodiments, the one or several rules and/or conditions can identifyportions of the data packet for providing to the view module 674 and/orportions of the data packet for not providing to the view module.

In some embodiments, the presenter module 672 can, according to the oneor more rules and/or conditions, generate and transmit an electronicmessage containing all or portions of the data packet to the view module674. The view module 674 can receive these all or portions of the datapacket and can provide all or portions of this information to the userof the user device 106 associated with the view module 674 via, forexample, the I/O subsystem 526. In some embodiments, as part of theproviding of all or portions of the data packet to the user of the viewmodule 674, one or several user responses can be received by the viewmodule 674. In some embodiments, these one or several user responses canbe received via the I/O subsystem 526 of the user device 106.

After one or several user responses have been received, the view module674 can provide the one or several user responses to the responseprocessor 678. In some embodiments, these one or several responses canbe directly provided to the response processor 678, and in someembodiments, these one or several responses can be provided indirectlyto the response processor 678 via the message channel 412.

After the response processor 678 receives the one or several responses,the response processor 678 can determine whether the responses aredesired responses and/or the degree to which the received responses aredesired responses. In some embodiments, the response processor can makethis determination via, for example, use of one or several techniques,including, for example, natural language processing (NLP), semanticanalysis, or the like.

In some embodiments, the response processor can determine whether aresponse is a desired response and/or the degree to which a response isa desired response with comparative data which can be associated withthe data packet. In some embodiments, this comparative data cancomprise, for example, an indication of a desired response and/or anindication of one or several undesired responses, a response key, aresponse rubric comprising one criterion or several criteria fordetermining the degree to which a response is a desired response, or thelike. In some embodiments, the comparative data can be received as aportion of and/or associated with a data packet. In some embodiments,the comparative data can be received by the response processor 678 fromthe presenter module 672 and/or from the message channel 412. In someembodiments, the response data received from the view module 674 cancomprise data identifying the user and/or the data packet or portion ofthe data packet with which the response is associated. In someembodiments in which the response processor 678 merely receives dataidentifying the data packet and/or portion of the data packet associatedwith the one or several responses, the response processor 678 canrequest and/or receive comparative data from the database server 104,and specifically from the content library database 303 of the databaseserver 104.

After the comparative data has been received, the response processor 678determines whether the one or several responses comprise desiredresponses and/or the degree to which the one or several responsescomprise desired responses. The response processor can then provide thedata characterizing whether the one or several responses comprisedesired response and/or the degree to which the one or several responsescomprises desired responses to the message channel 412. The messagechannel can, as discussed above, include the output of the responseprocessor 678 in the data stream 690 which can be constantly output bythe message channel 412.

In some embodiments, the model engine 682 can subscribe to the datastream 690 of the message channel 412 and can thus receive the datastream 690 of the message channel 412 as indicated in FIG. 9A. The modelengine 682 can monitor the data stream 690 to identify data and/orevents relevant to the operation of the model engine. In someembodiments, the model engine 682 can monitor the data stream 690 toidentify data and/or events relevant to the determination of whether aresponse is a desired response and/or the degree to which a response isa desired response.

When a relevant event and/or relevant data are identified by the modelengine, the model engine 682 can take the identified relevant eventand/or relevant data and modify one or several models. In someembodiments, this can include updating and/or modifying one or severalmodels relevant to the user who provided the responses, updating and/ormodifying one or several models relevant to the data packet associatedwith the responses, and/or the like. In some embodiments, these modelscan be retrieved from the database server 104, and, in some embodiments,can be retrieved from the model data source 309 of the database server104.

After the models have been updated, the updated models can be stored inthe database server 104. In some embodiments, the model engine 682 cansend data indicative of the event of the completion of the model updateto the message channel 412. The message channel 412 can incorporate thisinformation into the data stream 690 which can be received by therecommendation engine 686. The recommendation engine 686 can monitor thedata stream 690 to identify data and/or events relevant to the operationof the recommendation engine 686. In some embodiments, therecommendation engine 686 can monitor the data stream 690 to identifydata and/or events relevant to the updating of one or several models bythe model engine 682.

When the recommendation engine 686 identifies information in the datastream 690 indicating the completion of the summary model process 680for models relevant to the user providing the response and/or for modelsrelevant to the data packet provided to the user, the recommendationengine 686 can identify and/or select a next data packet for providingto the user and/or to the presentation process 470. In some embodiments,this selection of the next data packet can be performed according to oneor several rules and/or conditions. After the next data packet has beenselected, the recommendation engine 686 can provide information to themodel engine 682 identifying the next selected data packet and/or to themessage channel 412 indicating the event of the selection of the nextcontent item. After the message channel 412 receives informationidentifying the selection of the next content item and/or receives thenext content item, the message channel 412 can include this informationin the data stream 690 and the process discussed with respect to FIG. 9Acan be repeated.

With reference now to FIG. 9B, a schematic illustration of a secondembodiment of communication or processing that can be in the platformlayer 654 and/or applications layer 656 via the message channel 412 isshown. In the embodiment depicted in FIG. 9B, the data packet providedto the presenter module 672 and then to the view module 674 does notinclude a prompt for a user response and/or does not result in thereceipt of a user response. As no response is received, when the datapacket is completed, nothing is provided to the response processor 678,but rather data indicating the completion of the data packet is providedfrom one of the view module 674 and/or the presenter module 672 to themessage channel 412. The data is then included in the data stream 690and is received by the model engine 682 which uses the data to updateone or several models. After the model engine 682 has updated the one orseveral models, the model engine 682 provides data indicating thecompletion of the model updates to the message channel 412. The messagechannel 412 then includes the data indicating the completion of themodel updates in the data stream 690 and the recommendation engine 686,which can subscribe to the data stream 690, can extract the dataindicating the completion of the model updates from the data stream 690.The recommendation engine 686 can then identify a next one or severaldata packets for providing to the presenter module 672, and therecommendation engine 686 can then, either directly or indirectly,provide the next one or several data packets to the presenter module672.

With reference now to FIG. 9C, a schematic illustration of an embodimentof dual communication, or hybrid communication, in the platform layer654 and/or applications layer 656 is shown. Specifically, in thisembodiment, some communication is synchronous with the completion of oneor several tasks and some communication is asynchronous. Thus, in theembodiment depicted in FIG. 9C, the presenter module 972 communicatessynchronously with the model engine 682 via a direct communication 692and communicates asynchronously with the model engine 682 via themessage channel 412.

Specifically, and with reference to FIG. 9C, the presenter module 672can receive and/or select a data packet for presentation to the userdevice 106 via the view module 674. In some embodiments, the presentermodule 672 can identify all or portions of the data packet that can beprovided to the view module 674 and portions of the data packet forretaining from the view module 674. In some embodiments, the presentermodule can provide all or portions of the data packet to the view module674. In some embodiments, and in response to the receipt of all orportions of the data packet, the view module 674 can provide aconfirmation of receipt of the all or portions of the data packet andcan provide those all or portions of the data packet to the user via theuser device 106. In some embodiments, the view module 674 can providethose all or portions of the data packet to the user device 106 whilecontrolling the inner loop of the presentation of the data packet to theuser via the user device 106.

After those all or portions of the data packet have been provided to theuser device 106, a response indicative of the completion of one orseveral tasks associated with the data packet can be received by theview module 674 from the user device 106, and specifically from the I/Osubsystem 526 of the user device 106. In response to this receive, theview module 674 can provide an indication of this completion status tothe presenter module 672 and/or can provide the response to the responseprocessor 678.

After the response has been received by the response processor 678, theresponse processor 678 can determine whether the received response is adesired response. In some embodiments, this can include, for example,determining whether the response comprises a correct answer and/or thedegree to which the response comprises a correct answer.

After the response processor has determined whether the receivedresponse is a desired response, the response processor 678 can providean indicator of the result of the determination of whether the receivedresponse is a desired response to the presenter module 672. In responseto the receipt of the indicator of whether the result of thedetermination of whether the received response is a desired response,the presenter module 672 can synchronously communicate with the modelengine 682 via a direct communication 692 and can asynchronouslycommunicate with model engine 682 via the message channel 412. In someembodiments, the synchronous communication can advantageously includetwo-way communication between the model engine 682 and the presentermodule 672 such that the model engine 682 can provide an indication tothe presenter module 672 when model updating is completed by the modelengine.

After the model engine 682 has received one or both of the synchronousand asynchronous communications, the model engine 682 can update one orseveral models relating to, for example, the user, the data packet, orthe like. After the model engine 682 has completed the updating of theone or several models, the model engine 682 can send a communication tothe presenter module 672 indicating the completion of the updated one orseveral modules.

After the presenter module 672 receives the communication indicating thecompletion of the updating of the one or several models, the presentermodule 672 can send a communication to the recommendation engine 686requesting identification of a next data packet. As discussed above, therecommendation engine 686 can then retrieve the updated model andretrieve the user information. With the updated models and the userinformation, the recommendation engine can identify a next data packetfor providing to the user, and can provide the data packet to thepresenter module 672. In some embodiments, the recommendation engine 686can further provide an indication of the next data packet to the modelengine 682, which can use this information relating to the next datapacket to update one or several models, either immediately, or afterreceiving a communication from the presenter module 672 subsequent tothe determination of whether a received response for that data packet isa desired response.

With reference now to FIG. 9D, a schematic illustration of oneembodiment of the presentation process 670 is shown. Specifically, FIG.9D depicts multiple portions of the presenter module 672, namely, theexternal portion 673 and the internal portion 675. In some embodiments,the external portion 673 of the presenter module 672 can be located inthe server, and in some embodiments, the internal portion 675 of thepresenter module 672 can be located in the user device 106. In someembodiments, the external portion 673 of the presenter module can beconfigured to communicate and/or exchange data with the internal portion675 of the presenter module 672 as discussed herein. In someembodiments, for example, the external portion 673 of the presentermodule 672 can receive a data packet and can parse the data packet intoportions for providing to the internal portion 675 of the presentermodule 672 and portions for not providing to the internal portion 675 ofthe presenter module 672. In some embodiments, the external portion 673of the presenter module 672 can receive a request for additional dataand/or an additional data packet from the internal portion 675 of thepresenter module 672. In such an embodiment, the external portion 673 ofthe presenter module 672 can identify and retrieve the requested dataand/or the additional data packet from, for example, the database server104 and more specifically from the content library database 104.

With reference now to FIG. 10A, a flowchart illustrating one embodimentof a process 440 for data management is shown. In some embodiments, theprocess 440 can be performed by the content management server 102, andmore specifically by the presentation system 408 and/or by thepresentation module or presentation engine. In some embodiments, theprocess 440 can be performed as part of the presentation process 670.

The process 440 begins at block 442, wherein a data packet isidentified. In some embodiments, the data packet can be a data packetfor providing to a student-user. In some embodiments, the data packetcan be identified based on a communication received either directly orindirectly from the recommendation engine 686.

After the data packet has been identified, the process 440 proceeds toblock 444, wherein the data packet is requested. In some embodiments,this can include the requesting of information relating to the datapacket such as the data forming the data packet. In some embodiments,this information can be requested from, for example, the content librarydatabase 303. After the data packet has been requested, the process 440proceeds to block 446, wherein the data packet is received. In someembodiments, the data packet can be received by the presentation system408 from, for example, the content library database 303.

After the data packet has been received, the process 440 proceeds toblock 448, wherein one or several data components are identified. Insome embodiments, for example, the data packet can include one orseveral data components which can, for example, contain different data.In some embodiments, one of these data components, referred to herein asa presentation component, can include content for providing to thestudent user, which content can include one or several requests and/orquestions and/or the like. In some embodiments, one of these datacomponents, referred to herein as a response component, can include dataused in evaluating one or several responses received from the userdevice 106 in response to the data packet, and specifically in responseto the presentation component and/or the one or several requests and/orquestions of the presentation component. Thus, in some embodiments, theresponse component of the data packet can be used to ascertain whetherthe user has provided a desired response or an undesired response.

After the data components have been identified, the process 440 proceedsto block 450, wherein a delivery data packet is identified. In someembodiments, the delivery data packet can include the one or severaldata components of the data packets for delivery to a user such as thestudent-user via the user device 106. In some embodiments, the deliverypacket can include the presentation component, and in some embodiments,the delivery packet can exclude the response packet. After the deliverydata packet has been generated, the process 440 proceeds to block 452,wherein the delivery data packet is provided to the user device 106 andmore specifically to the view module 674. In some embodiments, this caninclude providing the delivery data packet to the user device 106 via,for example, the communication network 120.

After the delivery data packet has been provided to the user device 106,the process 440 proceeds to block 454, wherein the data packet and/orone or several components thereof are sent to and/or provided to theresponse processor 678. In some embodiments, this sending of the datapacket and/or one or several components thereof to the responseprocessor can include receiving a response from the student-user, andsending the response to the student-user to the response processorsimultaneous with the sending of the data packet and/or one or severalcomponents thereof to the response processor. In some embodiments, forexample, this can include providing the response component to theresponse processor. In some embodiments, the response component can beprovided to the response processor from the presentation system 408.

With reference now to FIG. 10B, a flowchart illustrating one embodimentof a process 460 for evaluating a response is shown. In someembodiments, the process can be performed as a part of the responseprocess 676 and can be performed by, for example, the response system406 and/or by the response processor 678. In some embodiments, theprocess 460 can be performed by the response system 406 in response tothe receipt of a response, either directly or indirectly, from the userdevice 106 or from the view module 674.

The process 460 begins at block 462, wherein a response is receivedfrom, for example, the user device 106 via, for example, thecommunication network 120. After the response has been received, theprocess 460 proceeds to block 464, wherein the data packet associatedwith the response is received. In some embodiments, this can includereceiving all or one or several components of the data packet such as,for example, the response component of the data packet. In someembodiments, the data packet can be received by the response processorfrom the presentation engine.

After the data packet has been received, the process 460 proceeds toblock 466, wherein the response type is identified. In some embodiments,this identification can be performed based on data, such as metadataassociated with the response. In other embodiments, this identificationcan be performed based on data packet information such as the responsecomponent.

In some embodiments, the response type can identify one or severalattributes of the one or several requests and/or questions of the datapacket such as, for example, the request and/or question type. In someembodiments, this can include identifying some or all of the one orseveral requests and/or questions as true/false, multiple choice, shortanswer, essay, or the like.

After the response type has been identified, the process 460 proceeds toblock 468, wherein the data packet and the response are compared todetermine whether the response comprises a desired response and/or anundesired response. In some embodiments, this can include comparing thereceived response and the data packet to determine if the receivedresponse matches all or portions of the response component of the datapacket, to determine the degree to which the received response matchesall or portions of the response component, to determine the degree towhich the receive response embodies one or several qualities identifiedin the response component of the data packet, or the like. In someembodiments, this can include classifying the response according to oneor several rules. In some embodiments, these rules can be used toclassify the response as either desired or undesired. In someembodiments, these rules can be used to identify one or several errorsand/or misconceptions evidenced in the response. In some embodiments,this can include, for example: use of natural language processingsoftware and/or algorithms; use of one or several digital thesauruses;use of lemmatization software, dictionaries, and/or algorithms; or thelike.

After the data packet and the response have been compared, the process460 proceeds to block 470 wherein response desirability is determined.In some embodiments this can include, based on the result of thecomparison of the data packet and the response, whether the response isa desired response or is an undesired response. In some embodiments,this can further include quantifying the degree to which the response isa desired response. This determination can include, for example,determining if the response is a correct response, an incorrectresponse, a partially correct response, or the like. In someembodiments, the determination of response desirability can include thegeneration of a value characterizing the response desirability and thestoring of this value in one of the databases 104 such as, for example,the user profile database 301. After the response desirability has beendetermined, the process 460 proceeds to block 472, wherein an assessmentvalue is generated. In some embodiments, the assessment value can be anaggregate value characterizing response desirability for one or more aplurality of responses. This assessment value can be stored in one ofthe databases 104 such as the user profile database 301.

With reference now to FIG. 11, a schematic illustration of oneembodiment of the content network 700 is shown. In some embodiments, thecontent network 700, also referred to herein as the object network caninclude a plurality of edges 701 linking a plurality of nodes 702. Insome embodiments, the edges 701 can link the nodes 702 in prerequisiterelationships such that a single edge 701 links a pair of nodes 702, oneof which of the pair of nodes 702 is a prerequisite to the other of thepair of nodes 702. In FIG. 11, this prerequisite relationship isindicated by the direction of the arrow of the edge 701 linking a pairof nodes 702 such that the origin of the arrow is at the prerequisitenode and the head of the arrow is at the non-prerequisite node.

In the embodiment depicted in FIG. 11, these nodes 702 include node 1,node 2, node 3, node 4, node 5, and node 6. As further depicted in FIG.11, some of the nodes 702 such as, for example, node 1 may beunassociated with content whereas others of the nodes 702 such as, forexample, node 2, node 3, node 4, node 5, and node 6 can be associatedwith content 704. Specifically, node 2 is associated with content b,node 3 is associated with content c, node 4 is associated with contentletter d, node 5 is associated with placeholder content also referred toherein up placeholder node, and node 6 is associated with content letterf. In some embodiments, a placeholder node is a node whose content isnot fixed but can be, for example, adaptively selected from, forexample, a set of potential content or content objects that can be foundin the database server 104. In some embodiments, this content can beadaptively selected based on one or several predictive or statisticalmodels relating to content, the user, user skill level, or the like.

In some embodiments, the content of a placeholder node can be selectedfrom one or several databases relevant to that placeholder node. Thesedatabases can be stored within, for example, the content library datastore 303. In some embodiments, the content for presentation to a userat a placeholder node can be determined by the recommendation engine 686based on statistical models which can be, for example, updated by themodel engine based on data relating to one or several users pastperformance.

In some embodiments, some or all of the nodes 702 can be associated witha guard condition, also referred to herein as a gate condition, asindicated by conditions 706. In some embodiments, the guard conditioncan identify a prerequisite for entering and/or exiting a node 702.These guard conditions can, for example, predicate entry into a node 702on a user's mastery and/or failure to master one or several previousnodes, objectives such as objective one 708, skill levels, or the like.In some embodiments, these guard conditions can predicate entry into andnode 702 based on whether one or several responses to a previous nodeand/or to an assessment were correct or incorrect, or on the ratio ofcorrect incorrect responses to a previous node or assessment. In someembodiments, these guard conditions can predicate entry into a node 702based on the completion of a previous node 702 such as, for example,entry into node 3 is predicated on the completion of node 2. In someembodiments, each of these guard conditions can be associated with anode 702 and/or on edge 701. In some embodiments, each guard conditionis associated with a node 702. In some embodiments, this can result inan entry in a database of nodes 702 identifying a specific node 702including data relating to and/or identifying one or several guardconditions and/or one or several pointers to those one or several guardconditions.

In some embodiments, some or all of the nodes 702 of the content network700 can be arranged into one or several loops 703. In some embodiments,the duration of the loop can be based on one or several guard conditionsdetermining when a user can exit the loop and/or on criteria identifyingthe minimum and/or maximum number of cycles that a user can remain inthe loop. Specifically, as shown in FIG. 700, node 1 comprises anindicator of the maximum number of cycles that a user can remain in theloops 703, and specifically indicates that the maximum number of cyclesthat a user can remain in the loops 703 is 2. In some embodiments, forexample, the recommendation engine directing and/or controlling themovement of the user through the content network 700 can comprise acount that can be incremented each time that a user passes through aloop. This count can be compared to the indicator of the minimum and/ormaximum number of cycles that user can remain in the loop to determinewhether the user must be readmitted through the loop or whether the usershould be prohibited from readmission into the loop.

As further seen in FIG. 11, node 1 comprises an edge 701 connecting node1 to node 6. In some embodiments, when the count reaches the indicatorof the maximum number of cycles that a user can remain in the loops, theuser is then directed from node 1 to node 6.

With reference now to FIG. 12, a flowchart illustrating one embodimentof a process 800 for providing content to a user with two recommendationengines is shown. The process 800 can be performed by all or portions ofthe content distribution network 100, and can be specifically performedusing the engines and/or modules shown in FIGS. 9A to 9D. In oneembodiment, the process 800 can be performed by the server 102 of thecontent distribution network using some or all of the engines and/ormodules shown in FIGS. 9A to 9D.

The process 800 begins a block 802 wherein response data is received. Insome embodiments, the response data can be received by the presentermodule 672 and specifically by the external module 673 of the presentermodule 672. In some embodiments, the response data can be received bythe presenter module 672 from the user device 106 via the view module674, and in some embodiments, the response that can be received by theexternal module 673 of the presenter module 672 from the user device 106via the view module 674 and the internal module 675. In someembodiments, the response data can comprise a user response and/or dataidentifying a user response received for one or several previouslyprovided data packets, assessments, or the like.

In some embodiments, and in response to the receipt of the responsedata, the presenter module 672 and/or the external module 673 of thepresenter module 672 can assess the response data to determine whetherthe response comprises a correct or incorrect response, whether the userhas mastered one or several objectives are skills relating to theresponse, whether the user has completed the previous data packet, orthe like. In some embodiments, and in response to the receipt of theresponse data, the presenter module 672 can provide the response data tothe response processor 678 for processing to determine the correctnessor incorrectness of the response. In some embodiments, the response datacan be directly provided to the response processor 678 and in someembodiments, the response data can be provided to the response processor678 via the message channel 412. In some embodiments, and after theresponse processor 678 has evaluated the response data, the responseprocessor 678 can provide data indicative of the result of theevaluation of the response data performed by the response processor tothe presenter module 672. In some embodiments, the data indicative ofthe result of the evaluation can be directly provided to the presentermodule 672 and/or indirectly provided to the presenter module 672 viathe message channel 412.

After the response data has been received, the process 800 proceeds toblock 804 wherein the response data is provided to the firstrecommendation engine. In some embodiments in which the firstrecommendation engine comprises the presenter module 672 andspecifically the external module 673 of the presenter module 672, thereceipt of the response by the presenter module 672 can comprise theproviding of the response data to the first recommendation engine. Insome embodiments in which the first recommendation engine comprises thepresenter module 672 and specifically the external module 673 of thepresenter module 672, the receipt, by the presenter module 672, of thedata indicative of the result of the evaluation from the responseprocessor 678 can comprise the providing of the response data to thefirst recommendation engine. In some embodiments in which the firstrecommendation engine is separate from presenter module 672, thepresenter module 672 can provide data indicative of the result of theevaluation to that first recommendation engine in the form of one orseveral electrical signals.

After the response data has been provided to the first recommendationengine, the process 800 proceeds to block 806 wherein a next node isidentified. In some embodiments, this next node is identified accordingto the application of one or several rules or conditions to the userhistory of the user source of response data received in block 802. Insome embodiments, the next node can be identified according to theapplication of one or several rules or conditions to the data indicativeof the result of the evaluation of the response data by the responseprocessor 678. In some embodiments, this next node can be identified bythe first recommendation engine.

After the next node has been identified, the process 800 proceeds todecision state 808 wherein it is determined if the next node comprises aplaceholder node. In some embodiments this determination can includedetermining whether the next node is associated with specific, constantcontent, or if the next node is associated with adaptively selectedcontent. If it is determined that the next node is a placeholder node,then the process 800 proceeds to block 810 wherein a secondrecommendation engine is alerted. In some embodiments, the secondrecommendation engine can comprise a recommendation engine configured toadaptively select content for providing to a user in a placeholder node.In some embodiments, the adaptive selection of content is based on oneor several statistical models representing one or several attributes ofthe content, the user history, a user skill level, a user learningstyle, or the like. In some embodiments, the alerting of the secondrecommendation engine can comprise the sending of one or severalelectrical signals to the second recommendation engine which one orseveral electrical signals can comprise, for example, user data,response data, or the like.

After the second recommendation has been alerted, the process 800proceeds to block 812 wherein the next node content is selected. In someembodiments, the next node content can be selected according to aplaceholder algorithm which is described in process 840 of FIG. 14. Insome embodiments, this can include the identification of next contentfrom, for example, a database of potential next content based on thestatistical models, which database of next content can be located in thedatabase server 104, and particularly in the content library database303. The selection of the next node content can be made by the secondrecommendation engine which can reside in, for example, recommendationengine 686.

After the next node content has been selected, or returning again todecision state 808 if it is determined the next node is not aplaceholder node, then the process 800 proceeds to block 814 where nextnode content is received or retrieved. In embodiments in which the nextnode is not a placeholder node, then the next content can be the contentassociated with the next node. In the embodiments in which the next nodeis a placeholder node, then the next content can be the next nodecontent selected in block 812. In some embodiments, the next nodecontent can be retrieved from the database server 104, and particularlyfrom the content library database 303.

After the next node content has been retrieved, the process 800 proceedsto block 816, wherein the next node content is provided to the user. Insome embodiments, this can include generating a communication containingthe next node content, which communication can comprise a plurality ofelectrical signals and sending the communication to the user device 106from which the response data was received in block 802. In someembodiments, the next node content is provided to the user via the userdevice 106, and is particularly provided to the user device 106 from thepresenter module 672 via the view module 674. In some embodiments, thenext node content is provided to the user device from the presentermodule 672, and particularly from the external module 673 of thepresenter module 672 to the internal module 675 of the presenter module672, then from the internal module 675 of the presenter module 672 tothe view module 674, and then to the user via the I/O subsystem 526 ofthe user device 106.

With reference now to FIG. 13, a flowchart illustrating one embodimentof a process 820 for identifying the next node is shown. The process 820can be performed by all or portions of the content distribution network100 including some or all of the engines are modules shown in FIGS. 9Ato 9D. In some embodiments, the process 820 can be performed by thefirst recommendation engine which can be, in some embodiments, locatedin one or both of the presentation module 672 and the recommendationengine 686. In some embodiments, the process 820 can be performed as apart of or in place of step 806 FIG. 12.

The process 820 begins at block 824 wherein the user's location contentnetwork is determined. In some embodiments, this can include determiningthe content network in which the user is located, and/or selecting oneof one or several content networks in which the user is located. In someembodiments, the user is the user source of the response they receivedin block 802. In some embodiments, this user can be identified based onthe response they received in block 802 and/or based on, for example, auser identifier provided at the time of user login before the step ofblock 802 of FIG. 8. In some embodiments, the determination of theuser's content network and/or location in the content network can bebased on the identification of the data packet and/or node 702associated with the response data received in block 802. In someembodiments, the user's location in the content network can bedetermined by identifying the data packet and/or node 702 associatedwith the response they received 802 and then identifying that datapacket and/or node 702 in the content network.

After the location the content network is been identified, the process820 proceeds to block 826 wherein user history data is retrieved. Insome embodiments, the user history data can be retrieved for the usersource of the response received in block 802. In some embodiments, thisuser history can identify user performance on one or several previousassessments, data packets, tasks, nodes, or the like. In someembodiments, the user history can be retrieved from the user profiledatabase 301 or from another location on the database server 104.

After the user history has been retrieved, the process 820 proceeds toblock 828 wherein one or several potential next nodes are identified. Asis shown in FIG. 11 with respect to node 1, in some embodiments thepotential next nodes can comprise a plurality of next nodes. As is shownin FIG. 11 with respect to node 2, in some embodiments, the potentialnext node can comprise a single next node. In some embodiments,potential next nodes can be identified by identifying the node of theuser's location in the content network, also referred to herein as anorigin node, identifying edges extending from the node of the user'slocation, and identifying non-prerequisite nodes to the node of theuser's location connected to the node of the user's location via theidentified edges. In some embodiments, the potential next nodes can beidentified by the first recommendation engine.

After the potential next nodes have been identified, the process 820proceeds to block 830 wherein the conditions for the identifiedpotential next nodes are retrieved. In some embodiments, each of thesegate conditions can identify one or several requirements for advancingto a least one of the identified potential next nodes. These gateconditions can be retrieved from the database server 104 andspecifically from the content library database 303. In some embodiments,each of the gate conditions can be stored and/or represented via, forexample, a pointer, in the content library database 303 in associationwith its node. In some embodiments, the gate conditions can be retrievedfrom the database server 104 by querying the database server 104 fourgate conditions associated with the identified potential next nodes.

After the gate conditions have been retrieved, the process 820 proceedsto block 832 when the next node is identified by application of all orportions of the user history to the retrieved gate conditions. In someembodiments, this can include comparing and/or applying user historydata relevant to one or several data packets and/or assessmentspreviously presented and/or provided to the user to the gate conditionsretrieved in block 830. In some embodiments, this can further includecomparing and/or applying user history data relevant to the responsereceived in block 802 to the gate conditions retrieved in block 830. Insome embodiments, this comparison and/or application of user historydata in gate conditions can result in the identification of one node asthe next node.

In one embodiment, for example, the application of the user history tothe gate conditions can comprise: identifying the set of potential nextnodes; selecting one of the potential next nodes; identifying the guardconditions of the selected potential next node; comparing the userhistory to the guard conditions of the potential next node, andassociating a value with the potential next node based on the result ofthe comparison of the user history to the guard conditions of thatpotential next node. In some embodiments, a first value can beassociated with the selected potential next node when a comparison ofthe guard conditions of the selected next node to the user historyindicates that all of the guard conditions have been met and the usercan enter the selected next node. Further, in some embodiments, a secondvalue can be associated with the selected potential next node when acomparison of the guard conditions of the selected next node to the userhistory indicates that not all of the guard conditions have been met andthe user cannot enter the selected next node. In some embodiments, thesesteps can be repeated until all of the potential next nodes have beenselected. Alternatively, in some embodiments, these steps can berepeated until a selected potential next node is associated with thefirst value. In such an embodiment, the first selected potential nextnode associated with the first value is identified as the next node.After the next node has been identified, the process 800 proceeds toblock 834 and continues to block 808 of FIG. 12.

With reference now to FIG. 14, a flowchart illustrating one embodimentof a process 840 for selecting next node content is shown. The process840 can be performed by all or portions of the content distributionnetwork 100 including some or all of the engines modules shown in FIGS.9A to 9D. In some embodiments, the process 840 can be performed by thesecond recommendation engine which can be, in some embodiments, locatedin the recommendation engine 686. In some embodiments, the process 840can be performed as a part of or in place of step 812 in FIG. 12.

The process 840 begins at block 842 wherein the user is identified. Insome embodiments, the user can be identified based on the responsereceived in block 802 and/or based on, for example, a user identifierprovided at the time of user login before the step of block 802 of FIG.8. After the user has been identified, the process 840 proceeds to block844 wherein user history data is retrieved. In some embodiments, theuser history data can be retrieved for the user source of the responsereceived in block 802. In some embodiments, this user history canidentify user performance on one or several previous assessments, datapackets, tasks, nodes, or the like. In some embodiments, the userhistory can be retrieved from the user profile database 301 or fromanother location on the database server 104.

After the user history has been retrieved, the process 840 proceeds toblock 846 wherein potential next node content is identified. In someembodiments, this can comprise identifying the database of potentialnext node content associated with the placeholder node. In someembodiments, this database of potential next node content can be storedin the database server 104, and particularly in the content librarydatabase 303 of the database server 104.

After the potential next node content has been identified, the process840 proceeds to block 848 wherein one or several predictive models arereceived or retrieved. In some embodiments, these models can comprise aplurality of statistical models stored in a database server. In someembodiments, at least one of the statistical models identifies one of: askill level or a discrimination of the next data packet. In someembodiments, these statistical models can include one or severalevidence models, risk models, skill models, or the like. In someembodiments, an evidence model can be a mathematically-based statisticalmodel. The evidence model can be based on, for example, Item ResponseTheory (IRT), Bayesian Network (Bayes net), Performance Factor Analysis(PFA), or the like. The evidence model can, in some embodiments, becustomizable to a user and/or to one or several content items.Specifically, one or several inputs relating to the user and/or to oneor several content items can be inserted into the evidence model. Insome embodiments, these predictive models can be retrieved from thedatabase server 104 and specifically from the model database 309 in thedatabase server 104.

After the predictive models have been received or retrieved, the process840 proceeds to block 850 wherein the next node content is selectedbased on the predictive models. In some embodiments, this can includeidentifying one or several features of the potential next node content,identifying one or several features in the user history, extractingthese one or several features, generating one or several parameters fromthe one or several features, and inputting some or all of the one orseveral features and/or one or several parameters into the retrievedpredictive models. In some embodiments, the model can then, based on theinput features and/or parameters, generate a model output identifyingone or several of the potential next node content for selection as thenext node content. In some embodiments, this can be performed by theserver 102 using the second recommendation engine that can be, forexample, located in the recommendation engine 636. After the next nodecontent has been selected, the process 840 continues to block 852 andthen proceeds to block 814 identified in FIG. 12.

With reference now to FIG. 15, a flowchart illustrating one embodimentof a process 900 for selecting next node content is shown. The process900 can be performed by all or portions of the content distributionnetwork 100, and can be specifically performed using the engines and/ormodules shown in FIGS. 9A to 9D. In one embodiment, the process 900 canbe performed by the server 102 of the content distribution network usingsome or all of the engines and/or modules shown in FIGS. 9A to 9D. Theprocess 900 begins at block 902 wherein user identification informationis received. In some embodiments, user identification information caninclude a username, password, login information, a unique useridentifier, or the like. In some embodiments, the user identificationinformation can be received by the user device 106 via the I/O subsystem526 of the user device 106 and can be provided to the server 102 via thecommunication subsystem(s) 532 of one or both of the user device 106 andthe server 102, and communication network 120. After the useridentification information has been received, the user associated withuser identification information can be identified. In some embodiments,this can include comparing the received user identification informationto information stored in the database server such as, for example, userprofile database 301. In some embodiments, for example, useridentification information can be uniquely associated with userinformation stored in the user profile database 301 and the user can beidentified by making the received user identification information withthat user information.

After the user identification information has been received, the process900 proceeds block 904 wherein the location of the user and the contentnetwork is identified and/or determined. In some embodiments, the userwhose location is in the content network is determined is the user forwhom user identification information was received in block 902. In someembodiments, the determination of the user's location in the contentnetwork can comprise, for example, retrieving user history dataidentifying the last completed node 702 for the user. In someembodiments, this user history data can be stored in the database server104, and specifically in the user profile database 301. In someembodiments, the determination user's location in the content networkcan comprise identifying the last response provided by the user tocontent associated with the node and identifying the node associatedwith that last response. In some embodiments, the determination of theuser's location in the content network can be performed by the server102, and more specifically by, for example, the presenter module 672.

After the location in the content network has been determined, theprocess 900 proceeds block 906 wherein user history is retrieved. Insome embodiments, the user history can comprise user history data thatcan be retrieved for the user for whom the user identificationinformation was received in block 902. In some embodiments, this userhistory can identify user performance on one or several previousassessments, data packets, tasks, nodes, or the like. This user historycan be, in some embodiments, specific to a single user such as the userfor whom the user identification information was received in block 902,or specific to a group of users. In some embodiments, the user historycan be retrieved from the user profile database 301 or from anotherlocation on the database server 104.

After the user history has been retrieved, the process 900 proceeds toblock 908 wherein one or several potential next nodes are identified. Insome embodiments, these one or several potential next nodes can compriseone or several non-prerequisite nodes directly linked to the node of theuser's current location in the content network by an edge 701. In someembodiments, these potential next nodes can be identified by identifyingedges extending from the node of the user's location in the contentnetwork, and identifying the nodes connected to the node of the user'scurrent location in the content network by the identified edges, andgenerating a subset of those nodes that are non-prerequisite nodes tothe node of the user's current location in the content network. In someembodiments, the potential next nodes can be identified by the server102, and more specifically by the recommendation engine 686 and/or thepresenter module 672.

After the potential next nodes are identified, the process 900 proceedsto block 910 wherein gate conditions are retrieved. In some embodiments,these gate conditions can be the gate conditions associated with thepotential next nodes identified in block 908. In some embodiments, thegate conditions can be retrieved from the database server 104, andspecifically from the content library database 303. In some embodiments,the key conditions can be retrieved by querying the content librarydatabase for gate conditions associated with the nodes identified aspotential next nodes.

After the gate conditions have been retrieved, the process 900 proceedsto block 912 wherein the next node is identified by application of theuser history to the gate conditions. In some embodiments, the next nodecan be selected by the first recommendation engine and/or the presentermodule 672. In some embodiments, the next node can be selected from theset of potential next nodes identified in block 908.

After the next node has been identified, the process 900 proceedsdecision state 914 wherein it is determined if the next node is aplaceholder node. This determination can include determining whether thenext node is associated with specific, constant content, or if the nextnode is associated with adaptively selected content. In someembodiments, for example, nodes 702 can be associated with a first valuewhen they are not placeholder nodes and with a second value when theyare placeholder nodes. In some embodiments, determining if the next nodeis a placeholder node can comprise determining if the next node isassociated with the first value or with the second value.

If it is determined that the next node is a placeholder node, then theprocess 900 proceeds to block 916 wherein one or more predictive modelsare retrieved. In some embodiments, these models can comprise aplurality of statistical models stored in a database server. In someembodiments, at least one of the statistical models identifies one of: askill level or a discrimination of the next data packet. In someembodiments, these statistical models can include one or severalevidence models, risk models, skill models, or the like. In someembodiments, an evidence model can be a mathematically-based statisticalmodel. The evidence model can be based on, for example, Item ResponseTheory (IRT), Bayesian Network (Bayes net), Performance Factor Analysis(PFA), or the like. The evidence model can, in some embodiments, becustomizable to a user and/or to one or several content items.Specifically, one or several inputs relating to the user and/or to oneor several content items can be inserted into the evidence model. Insome embodiments, these predictive models can be retrieved from thedatabase server 104 and specifically from the model database 309 in thedatabase server 104.

After the predictive models have been retrieved, the process 900proceeds to block 918 wherein potential placeholder content isretrieved. In some embodiments, the retrieval of potential placeholdercontent can include the identification of potential placeholder content.In some embodiments, and as discussed above, a placeholder node can beassociated with a set of content that can be selectively presented inthe place of the placeholder node. In some embodiments, the set ofcontent can be stored in the database server 104 and specifically in thecontent library database 303 of the database server. In someembodiments, the set of potential placeholder content can be identifiedthrough a query of a database containing the potential placeholdercontent for potential placeholder content associated with the selectednext placeholder node. In some embodiments, this query can be sent tothe database server 104 by the server 102. In some embodiments, thepotential placeholder content can be received by the server 102 from thedatabase server 104 in response to the query of the database server 104for potential placeholder content.

After the set of potential placeholder content has been identified, theprocess 900 proceeds to block 920 wherein placeholder content isselected. In some embodiments, the placeholder content is selected fromthe set of potential placeholder content via, for example, theapplication of one or more models, such as those stored in the modeldatabase 309, to attributes of the user for whom identificationinformation was received in block 902 and/or two attributes of theplaceholder content in the set of potential placeholder content. In someembodiments, these models can be updated by the model engine 682 basedon one or several previously received responses from one or severalusers and the updated models can be stored in the model database 309.

In some embodiments, these models can be used to adaptively select thenext placeholder content. In some embodiments, for example, this caninclude using one or more of the models to generate a prediction of theuser skill level, a prediction of the difficulty levels of one orseveral pieces of placeholder content in the set of potentialplaceholder content, a prediction of the amount of time required for theuser to complete one or several pieces of placeholder content in the setof placeholder content, a likelihood of the user successfully completingone or several pieces of placeholder content in the set of placeholdercontent, or the like. In some embodiments, for example, the piece ofplaceholder content can be selected that has a skill level or difficultylevel best corresponding to a skill level of the user. In someembodiments, for example, the piece of placeholder content can beselected that has a desired likelihood of the user successfullycompleting that piece of placeholder content. In some embodiments, thepiece of placeholder content can be selected by the secondrecommendation engine and/or the recommendation engine 686.

After the next content has been identified, and returning again todecision state 914 if it is determined that the next node is not aplaceholder node, then the process 900 proceeds to block 922 wherein thenext node is provided to the user. In some embodiments, the next nodecan comprise the node and/or associated content identified in block 912or in case the next node is a placeholder node, the next node cancomprise the next content identified in block 920.

In some embodiments, the next node and/or content associated with thenext node is provided to the user via the user device 106, and isparticularly provided to the user device 106 from the presenter module672 via the view module 674. In some embodiments, the next node contentis provided to the user device from the presenter module 672, andparticularly from the external module 673 of the presenter module 672 tothe internal module 675 of the presenter module 672, then from theinternal module 675 of the presenter module 672 to the view module 674,and then to the user via the I/O subsystem 526 of the user device 106.

With reference now to FIG. 16, a flowchart illustrating one embodimentof a process 1000 for operation of a two-part presentation engine isshown. In some embodiments, this process specifically relates to theoperation of the presenter module 672 as it interacts with the viewmodule 674 depicted in FIGS. 9A to 9D. In some embodiments, some or allof the steps of process 1000 can be performed by the external module 673of the presenter module 672. The process begins at block 1002 whereinthe data packet is received by a portion of the presentation engine, andspecifically by the presenter module 672. In some embodiments, dataidentifying the intended user/recipient of the received data packet canbe received simultaneous with the receipt of the data packet. In someembodiments, the data packet can be received by the presenter module 672from the recommendation engine 686, from the message Channel 412, and/orfrom the database server 104 or more specifically from the contentlibrary database 303.

After the data packet has been received, the process 1000 proceeds toblock 1004 wherein packet metadata is extracted from the data packet. Insome embodiments, the packet metadata comprises data identifying one orseveral attributes of the other data of the data packet. In someembodiments, for example, this metadata can identify one or severalportions of data in the data packet such as, for example, thepresentation portion of the data in the data packet that is intended forproviding to the user and the non-presentation portion of the data inthe data packet that is not intended for providing to the user. In someembodiments, for example, the non-presentation portion of the data inthe data packet can comprise answer data and/or data for use inevaluating one or several user responses to the data packet. In someembodiments, the metadata can further comprise information identifyingone or several guard conditions associated with the data packet. In someembodiments, the metadata can be extracted from the data packet receivedin block 1002 by a portion of the presentation engine and specificallyby the presenter module 672.

After the packet metadata has been extracted, the process 1000 toproceeds block 1006 wherein the delivery portions of the data packet areidentified. In some embodiments, this can include using the extractedpacket metadata to delineate between the delivery portions of the datapacket and the non-delivery portions of the data packet. After thedelivery portions of the data packet has been identified, the process1000 proceeds block 1008, wherein the non-delivery portions of the datapacket are extracted. In some embodiments, this can include removing thenon-delivery portions from data forming the payload of a potentialcommunication from the presenter module 672 to the view module 674.

After the non-delivery portions have been extracted from the datapacket, the process 1000 proceeds to block 1010 wherein the recipientinternal module 675 is identified by the external module 673 of thepresenter module 672. In embodiments in which the presenter module 672does not include an external module 673 and an internal module 675, step1010 can include the identifying of the recipient view module 674 by thepresenter module 672. In some embodiments, this can include identifyingthe internal module 675 and/or the view module 674 associated with theintended recipient of the data packet received in block 1002, whichintended recipient can be identified based on data identifying theintended recipient received simultaneous with the receipt of the datapacket in block 1002.

After the recipient internal module 675 and/or view module 674 has beenidentified, the process 1000 proceeds to block 1012 wherein the deliveryportion is provided to the identified recipient internal module 675and/or view module 674. In some embodiments, this can include thegeneration of one or several electrical signals by the external module673 and/or the presenter module 672 and sending these are severalelectrical signals to the recipient identified in block 1010. In someembodiments, these one or several electrical signals can comprise thedelivery portions identified in block 1006. In some embodiments, theseelectrical signals can be transmitted via the communication network 120and the communication subsystems 532 of the server 102 and/or of theuser device 106.

After the delivery portions have been provided, the process 1000proceeds to decision state 1014 wherein it is determined if a datarequest is received. In some embodiments, for example, the externalmodule 673 and/or the presenter module 672 can receive a request fromeither the internal module 675 and/or the view module 674 for additionaldata. In some embodiments, this request can be received in response tothe delivery of portions of the data packet in block 1012 and/orindependent of the delivery of portions of the data packet in block1012.

If it is determined that a data request has been received, then theprocess proceeds to block 1016 wherein a relevant data store isidentified. In some embodiments, this can include identifying one orseveral databases in the database server 104 containing the requesteddata. In some embodiments, these one or several databases can beidentified by querying the database server and/or one or several of thedatabases of the database server 104 such as the content librarydatabase 303 for the requested data. In some embodiments, in response tosuch a query, the database server 104 and/or the recipient databases ofthat query can respond and identify whether they contain the requesteddata. If the database and/or the database server 104 contains therequested data, then that database and/or the database server 104 isidentified as a relevant data store.

After the relevant data store has been identified, the process 1000proceeds to block 1018 wherein the additional data is requested. In someembodiments, this additional data can be requested from the identifiedrelevant data store by, for example, the server 102. After theadditional data has been requested, the process 1000 proceeds to block1020 wherein one or several additional data packets and/or the requesteddata is received. In some embodiments, this requested data and/or one orseveral additional requested data packets can be received by the server102 and specifically by the presenter module 672 or the external module673 of the presenter module 672 from the identified relevant data store.After the one or several additional data packets and/or the requesteddata has been received, the process 1000 continues to block 1004 andproceeds as outlined above.

Returning again to decision state 1014, if it is determined that noadditional data is requested or that no data request is received, thenthe process 1000 proceeds to block 1022 wherein a response is received.In some embodiments, this response can relate to the data packetreceived in block 1002, and in some embodiments, this response canrelate to the additional data packet received in block 1020. In someembodiments, this response can be received after the determination ofdecision state 1014 or at any other point in process 1000. In someembodiments, this response can be received from the user device 106 viathe communication network 120 in connection with the delivery portionsdelivered in block 1012. In some embodiments, this response can bereceived by the presenter module 672 from the view module 674 and insome embodiments, this response can be received by the external module673 from the internal module 675, which internal module 675 received theresponse from the view module 674.

After the responses have been received, the process 1000 proceeds toblock 1024 wherein a message is generated and sent to one or severalother components of the content distribution network 100. In someembodiments, this message can be one or several electrical signals andcan comprise data indicative of the received response and/or thereceived response. This message can be generated by the external module675 and/or the presenter module 672 and can be sent to, for example, themessage Channel 412 and/or the response processor 678, the model engine682, and/or the recommendation engine. In some embodiments, the messagecan be sent via one or several communication subsystems 532 in thecommunication network 120.

With reference now to FIG. 17, a flowchart illustrating one embodimentof a process 1100 for automated misconception identification andremediation is shown. The process 1100 can be performed by all orportions of the content distribution network 100, and can bespecifically performed using the engines and/or modules shown in FIGS.9A to 9D. In one embodiment, the process 1100 can be performed by theserver 102 of the content distribution network using some or all of theengines and/or modules shown in FIGS. 9A to 9D. The process begins ablock 1102 wherein a user cohort is identified. In some embodiments, theuser cohort comprises a group of similarly situated users. In someembodiments, user cohort can comprise one or several users belonging toa common group, class, club, level, or the like. In some embodiments,the cohort can be identified based on user inputs received by the server102 from, for example, user device 106 and/or supervisor device 110.

After the user cohort has been identified, the process 1100 proceedsblock 1104 wherein a data packet is provided to the users in the usercohort identified in block 1102. In some embodiments, the data packetcan be selected according to one of the processes described herein.Specifically, in some embodiments, the data packet can be selected bythe recommendation engine 686 and can be provided to the user via theuser device 106 and specifically via a combination of the I/O subsystem526, the view module 674, presenter module six or 72, and the userdevice 106.

After the data packet has been provided to the users in the user cohort,the process 1100 proceeds block 1106 wherein responses to the provideddata packet are received. In some embodiments, for example, a responsecan be received from each of the users in the user cohort in someembodiments, the response can be received from each of some of the usersin the user cohort. In some embodiments, the response can be received bythe server 102 and specifically by the presenter module 672 via the viewmodule 674, the communications subsystem 532, the I/O system 526, andthe user device 106.

After the responses have been received, the process 1100 proceeds toblock 1108 wherein the responses received in block 1106 are evaluated.In some embodiments, the evaluation of the responses can include thetranslation of each of the responses into an observable. In someembodiments, this translation of each of the responses into anobservable can include determining whether each of the receivedresponses comprises a correct response or an incorrect response,alternatively referred to herein as a desired response or an undesiredresponse respectively. In some embodiments, the responses can beevaluated by the response processor 678 according to comparative datastored in the content library database 303. In some embodiments, theevaluation of the responses can include associating each of the receivedresponses with the value, and specifically associating each of theresponses comprising a desired response with a first value andassociating each of the received responses comprising an undesiredresponse with a second value. In some embodiments, these values can bestored in association with their received response in the user profiledatabase 301 and/or in any other desired database in the database server104.

After the responses have been evaluated, the process 1100 proceeds toblock 1110 wherein user misconceptions are identified based on evaluatedresponses. In some embodiments, this can include identifying one orseveral misconceptions and/or identifying the users having those one orseveral misconceptions. In some embodiments, these user misconceptionscan be identified based on one or several commonalities in attributes ofresponses received in block 1106 as indicated by the evaluation of theresponses in block 1108. In some embodiments, the user misconceptionscan be identified by the server 102 and specifically by the responseprocessor 678, and/or the model engine 682.

After the user misconceptions have been identified, the process 1100proceeds to block 1112 wherein one or several remediations are selectedand provided to users identified with the misconception. In someembodiments, this can include identifying one or several data packetsassociated with the misconception such as, for example, the data packetprovided in block 1104. In some embodiments, the selecting and theproviding of the remediation can comprise the identification of theseone or several data packets associated with the misconception andgenerating and sending an alert to, for example, the supervisor device110 requesting modification and/or replacing of these one or severaldata packets. In some embodiments, this alert can be generated and sentas described above. In some embodiments, this can include selecting andproviding one or several data packets containing content configured toaddress and/or remedy the misconceptions. In some embodiments, the usersreceiving the remediation's can be user is identified as having a usermisconception in block 1110.

With reference now to FIG. 18, a flowchart illustrating one embodimentof a process 1130 for identifying a user misconception is shown. Theprocess 1130 can be performed as a part of or in the place of block 1110of FIG. 17. In some embodiments, the process 1130 can be performed bythe content distribution network 100 and/or components thereof includingthe server. In some embodiments, the process 1130 can be performed usingthe engines and/or modules shown in FIGS. 9A to 9D.

The process begins at block 1132 wherein one or several response cohortsare generated. In some embodiments, each of these one or severalresponse cohorts can identify users having similarly situated responses.In some embodiments, this can include grouping users in cohortsaccording to whether there provided response was a desired response oran undesired response and/or according to the reason for their responsebeing a desired response or an undesired response. In the context of aresponse to a select response question such as a multiple choicequestion or a true/false question, cohorts may be formed according tothe response selected by the user. In the case of a response to aconstructed response question, cohorts may be formed according to one orseveral user steps performed as part of the response, to user providedreasons supporting the response, or the like. In some embodiments, auser can be associated with the value indicative of the response cohortinto which they are placed. The response cohorts can be generated by theserver 100 to and specifically by the response processor 678.

After the response cohorts of been generated, the process 1130 proceedsto block 1134 wherein cohort percentages are determined. In someembodiments, a cohort percentage can comprise the percentage of users inthe user cohort identified in block 1102 that are found in each of theresponse cohorts generated in block 1132. In some embodiments, a cohortpercentage can comprise the percentage of response providing users inthe user cohort identified in block 1102 that are found in each of theresponse cohorts generated in block 1132. In some embodiments, thecohort percentages can be determined by the server 102 and specificallyby the response processor 678.

After the cohort percentages have been determined, the process 1130proceeds to block 1136 wherein a misconception threshold is retrieved.In some embodiments, the misconception threshold can delineate betweensituations in which the likelihood of the existence of a misconceptionis sufficiently minimal as to warrant no intervention and/or remediationand situations in which the likelihood of the existence of amisconception is sufficiently large as to warrant an intervention and/orremediation. In some embodiments, the misconception threshold can beretrieved from the database server 104 and specifically from thethreshold database 310 in the database server 104.

After the misconception threshold has been retrieved, the process 1130proceeds block 1138 wherein the cohort percentages are compared with themisconception threshold. In some embodiments, this comparison candetermine whether the cohort percentage indicates a sufficiently largelikelihood of the existence of a misconception to warrant anintervention and/or remediation. In some embodiments, if the comparisonof the cohort percentage and the misconception threshold indicates asufficiently large likelihood of the existence of misconception as towarrant an intervention, a first value can be associated with thatcohort, and if the comparison of the cohort percentage in themisconception threshold indicates an insufficiently large likelihood ofthe existence of a misconception to warrant an intervention, a secondvalue can be associated with that cohort. In some embodiments, thecohort percentage can be compared with the misconception threshold bythe server 102 including, for example, the response processor 678 and/orthe model engine 682.

After the cohort percentage has been compared to the misconceptionthreshold, the process 1130 proceeds to decision state 1140 wherein itis determined if a potential misconception has been identified. In someembodiments, this can include determining whether the first or secondvalue is associated with a response cohort. More specifically, this caninclude selecting one of the response cohorts and determining whetherthat selected one of the response cohorts is associated with the firstor second value. If the selected one of the response cohorts isassociate with the first value then a potential misconception isidentified, alternatively, if the selected one of the response cohortsis associated with a second value then no potential misconception hasbeen identified. In some embodiments, this can be repeated until each ofthe response cohorts has been evaluated to determine whether it isassociated with the potential misconception. If it is determined thatthe selected one of the potential cohorts is not associated with thepotential misconception, then the process 1130 can proceed to block 1141and await a next response. In some embodiments, after the next responsesbeen received, the process can proceed to block 1108 of FIG. 17.

Returning again to decision state 1140, if it is determined that apotential misconception has been identified, and the process 1130proceeds to block 1142 wherein metadata is updated. In some embodiments,this can include updating the user data of users in some or all of theresponse cohorts, and in some embodiments, this can include updatingmetadata relating to the data packet provided in, for example, block1104 of FIG. 17. After the metadata has been updated, the process 1130proceeds to block 1144 and continues to block 1112 of FIG. 17.

With reference now to FIG. 19, a flowchart illustrating one embodimentof a process 1200 for automatically triggering an intervention is shown.In some embodiments, this can comprise an intervention for an individualwith an identified misconception. In some embodiments, this interventioncan comprise a targeted intervention that can comprise additionalcontent, questions, assessments, or the like. In some embodiments, thisintervention can be provided in the form of one or several additionaldata packets. In some embodiments, this intervention can compriseinteraction with an AI (Artificial Intelligence or ArtificiallyIntelligent) agent. In some embodiments, the AI agent can comprise aquestion answering computing system, all or portions of an intelligenttutoring system, pedagogical agent, or the like. In some embodiments,the functioning of the AI agent can be predicated upon artificialintelligence, machine learning, or the like. In some embodiments, the AIagent can reside in the server 102 and/or another component of thecontent distribution network 100. In some embodiments, the process 1200can be performed by the server 102 and/or other component of the contentdistribution network 100 including, for example, the engines and/ormodules shown in FIGS. 9A to 9D.

The process 1200 begins a block 1202 wherein a user is selected. In someembodiments, the user can be selected from several users associated withuser data in the user profile database 301. In some embodiments, theuser can be selected by the server 102. After the user has beenselected, the process 1200 proceeds block 1204 wherein the user dataassociated with the user selected in block 1202 is retrieved. In someembodiments, this user data can comprise user metadata that can identifyone or several attributes of the user including evaluation results forone or several previous responses of the selected user. In someembodiments, the user metadata can be retrieved from the user profiledatabase 301 by, for example, the server 102.

After the user data has been retrieved, the process 1200 proceeds todecision state 1206 wherein it is determined if the selected user isassociated with a potential misconception. In some embodiments, this caninclude determining if the selected user belongs to a cohort associatedwith the first value indicative of a potential misconception. In someembodiments, this first value can be stored in the user metadata asindicated in block 1142 of FIG. 18. In some embodiments, determining ifthe selected user belongs to a cohort associate with the first valuecomprises extracting portions of the user metadata indicative of whetherthe user belongs to such a cohort.

In some embodiments, determining if the selected user is associated witha potential misconception can include determining if the selected useris associated with one or several nodes associated with a misconception.In some embodiments, for example, in which the process 1100 and 1130 ofFIGS. 17 and 18 have been performed, metadata associated with one orseveral data packets can identify these data packets as being previouslyassociated with a misconception. In some embodiments, determining if theselected user is associated with a potential misconception can includedetermining whether the selected user has traversed and/or receivedcontent associated with one or several nodes having metadata identifyingprevious associated misconceptions. In some embodiments, if the selecteduser is associated with one or several such nodes, a value indicating alikelihood of misconception can be generated based on, for example, acomparison of one or several attributes to attributes of users whopreviously had misconceptions. If this value indicating the likelihoodof misconception is sufficiently high, then the user can be flagged ashaving a potential misconception. If it is determined that the selecteduser has a potential misconception, then the process 1200 proceeds block1216 and awaits the next response.

Returning again to decision state 1206, if it is determined that theuser belongs to a cohort associated with the first value indicative ofpotential misconception, then the process 1200 proceeds to decisionstate 1208 where it is determined if the user metadata containsadditional data indicative of a misconception relevant to the cohortassociated with the first value indicative of a potential misconceptionto which the selected user belongs as determined in decision state 1206.In some embodiments, this can include identifying one or several otherresponses provided by the user selected in block 1202 and/or metadatarelating to one or several other responses provided by the user selectedin block 1202, and determining whether these one or several otherresponses are indicative of the relevant misconception. In someembodiments, this determination can be performed by the server 102

If one or several previous responses indicative of a misconceptionrelevant to the cohort associated with the first value and/or metadatarelating to one or several previous responses indicative of amisconception relevant to the cohort associated with the first value areidentified, then the process 1200 proceeds to block 1210 wherein themisconception threshold is retrieved.

In some embodiments, misconception threshold can be the same thresholdas retrieved in block 1136 of FIG. 18, and in some embodiments, themisconception threshold retrieved in block 1210 can be different thanthe misconception threshold retrieved in block 1136 of FIG. 18. In someembodiments, for example, the misconception threshold retrieved in block1136 of FIG. 18 can be used for identifying a misconception relevant toa data packet, and/or a misconception relevant to the user cohortidentified in block 1102 of FIG. 17.

As the misconception threshold identified in block 1210 of FIG. 19 isused for identifying a misconception in a single user, the thresholdvalue may be different than those using the above referenced othercircumstances. Similarly, as the user cohort identified in block 1102 ofFIG. 17 can, by itself, provide a statistically significant sample, amisconception can be identified relevant to a data packet and/or usercohort is identified in block 1102 of FIG. 17 based on a set ofresponses from that cohort selected in block 1102 of FIG. 17 to a singledata packet whereas the identification of a misconception for singleuser may require responses to a plurality of data packets. In someembodiments, the value of the misconception threshold can vary based onthe responses provided by the user and/or the metadata relating to theresponses provided by the user. In some embodiments, for example, ahigher threshold may be required for selected response type responsesdue to uncertainty resulting from the role of guessing in responding tosuch type responses. The misconception threshold can be retrieved by theserver 102 from the threshold database 310.

After the misconception threshold has been retrieved, the process 1200proceeds to block 1212 wherein the misconception threshold is comparedwith some or all the portions of the user metadata relevant to thepotential misconception associated with the cohort including the user.In some embodiments, this can include determining a likelihood of theexistence of a misconception based on the aggregate of some or all theuser responses relevant to that misconception. In some embodiments, thecomparison of the misconception threshold and some or all the portionsthe user data relevant to the potential misconception can be performedby the server 102.

After the misconception threshold and the user metadata relevant to themisconception have been compared, the process 1200 proceeds block 1214wherein it is determined if the misconception is confirmed. In someembodiments, this can include determining whether the some or all of themetadata associated with and/or relevant to the potential misconceptionindicated a sufficiently high a likelihood of a misconception as towarrant an intervention. If it is determined that an intervention iswarranted, then the process 1200 can proceed to block 1218 and anintervention can be triggered. In some embodiments, this interventioncan comprise a targeted intervention that can include the delivery ofcontent to remedy the one or several identified misconceptions. In someembodiments, this intervention can comprise one or several interactionswith an AI agent. In some embodiments, this can include generating andsending an alert to the AI agent, the user device 106 of the userselected in block 1202 and/or two the supervisor device 110 of thesupervisor of the user selected in block 1202. In some embodiments, thisalert can include computer code configured to trigger the automaticoperation of the AI agent and/or to automatically display one or severalpieces of data such as, for example, identification of the misconceptiondetermined in decision state 1214.

Returning again to decision state 1214, if it is determined that themisconception has not been confirmed, or returning to decision state1208, if it is determined that the user selected in block trouble to isnot associated with metadata relevant to the potential misconceptionassociated with the cohort in which the user belongs, then the process1200 can proceed to block 1220 wherein a supplemental data packet isidentified. In some embodiments, the supplemental data packet can beidentified and/or selected from the set of data packets contained in thecontent library database 303. In some embodiments, the supplemental datapacket can be selected based on the relevance of the anticipatedresponse to the selected data packet to determining whether the can beverified and/or that an intervention for the potential misconception iswarranted. In some embodiments, the supplemental data packet can beselected by the server 102 and/or the recommendation engine 686.

After the supplemental data packet has been selected, the process 1200can proceed to block 1222 wherein the data packet can be provided to theuser selected in block 1202. In some embodiments, the data packet can beprovided to the user selected in block 1202 by providing the data packetto the presenter module 672 which can then provide the data packet tothe view module 674 which can then provide the data packet to the userof the user device 106 via the I/O subsystem 526.

After the data packet has been provided to the user, the process 1200proceeds block 1224 wherein a response to the data packet provided inblock 1222 is received and evaluated. In some embodiments, the responsecan be received by the presenter module 672 via the view module 674, theI/O subsystem 526, and the user device 106, and the response can beevaluated by the response processor 678. In some embodiments, theresponse can be evaluated using comparative data retrieved from thedatabase server 104 and specifically from the content library database303.

After the responses been evaluated, the process 1200 proceeds block 1226wherein the user profile and/or user metadata is updated. In someembodiments, this can include the updating of one or several modelsassociated with the user and/or the user metadata to reflect the resultof the evaluation of the received response from block 1224. In someembodiments, the user metadata can be updated by the model engine 682.After the user profile and/or user metadata has been updated, theprocess 1200 returns to block 1210 and proceeds as outlined above.

A number of variations and modifications of the disclosed embodimentscan also be used. Specific details are given in the above description toprovide a thorough understanding of the embodiments. However, it isunderstood that the embodiments may be practiced without these specificdetails. For example, well-known circuits, processes, algorithms,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the embodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application-specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc., may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein, the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more memories for storing data, including read-only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to, portable orfixed storage devices, optical storage devices, and/or various otherstorage mediums capable of storing that contain or carry instruction(s)and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A system for content selection with a rules-basedrecommendation engine and an adaptive recommendation engine, the systemcomprising: memory comprising: a content library database comprising aplurality of nodes arranged in a content network, wherein the nodes inthe content network are pairwise connected via a plurality of edges,wherein some of the nodes in the content network are associated with adata packet and a guard condition, and wherein some of the nodes in thecontent network are associated with a database of placeholder content;and a model database comprising a plurality of models relating to atleast one of: a user skill level or a data packet difficulty level; auser device comprising: a first network interface configured to exchangedata via a communication network; and a first I/O subsystem configuredto convert electrical signals to user interpretable outputs via a userinterface; and one or more servers comprising a packet selection systemand a presentation system, wherein the one or more servers areconfigured to: receive response data from the user device; providereceived response data to a rules-based recommendation engine, whereinthe rules-based recommendation engine is configured to select a nextnode based on a current location in the content network, potential nextnodes, past response data, and one or several guard conditionsassociated with the potential next nodes; alert an adaptiverecommendation engine when a selected next node comprises a placeholdernode, wherein the placeholder node is associated with the database ofplaceholder content the adaptive recommendation engine performing:retrieving at least one model relevant to selection of next nodecontent; and selecting next node content based on an output of the atleast one model; generate subsequent output based at least in part onthe rules-based recommendation engine when the selected next node isdetermined to not be a placeholder node, the subsequent output linkeddirectly to the selected next node.
 2. The system of claim 1, whereinproviding received response data to the rules-based recommendationengine further comprises selecting a next node.
 3. The system of claim2, wherein selecting the next node comprises: identifying potential nextnodes; and retrieving guard conditions, wherein each guard conditiondefines one or several prerequisites for entry into one of the potentialnext nodes.
 4. The system of claim 2, wherein identifying potential nextnodes comprises: identifying the user's location in the content network,wherein the user's location in the content network comprises an originnode; identifying edges extending from the origin node; and identifyingnon-prerequisite nodes connected to the origin node via the identifiededges.
 5. The system of claim 4, wherein selecting the next node furthercomprises: identifying the user associated with the user device; andretrieving the user history from the memory.
 6. The system of claim 5,wherein selecting the next node further comprises application of theuser history to the guard conditions of the potential next nodes.
 7. Thesystem of claim 6, wherein application of the user history to the guardconditions of the potential next nodes comprises: (a) selecting one ofthe potential next nodes; (b) identifying of a guard conditionassociated with the selected potential next node; (c) comparing theguard condition with the user history; and (d) associating a first valuewith the selected one of the potential next nodes when the comparison ofthe guard condition with the user history indicates that the guardcondition is met.
 8. The system of claim 7, wherein steps (a)-(d) arerepeated for each of the potential next nodes.
 9. The system of claim 8,wherein selecting next node content based on an output of the at leastone model comprises: identifying the user associated with the userdevice; retrieving the user history; and identifying potential next nodecontent.
 10. The system of claim 9, wherein selecting next node contentbased on an output of the at least one model comprises: identifying oneor several features of at least one of: the potential next node contentor the user history; extracting the identified one or several features;and inputting some or all of the one or several features into theretrieved at least one model.
 11. The system of claim 10, whereinselecting next node content based on an output of the at least one modelcomprises generating an output with the retrieved at least one modelbased on the input some or all of the one or several features.
 12. Thesystem of claim 11, wherein the one or more servers are furtherconfigured to provide the selected next node content to the user device.13. The system of claim 12, wherein providing the selected next nodecontent to the user device comprises: generating a plurality ofelectrical signals comprising the selected next node content; andsending the electrical signal to the user device.
 14. The system ofclaim 1, wherein the rules-based recommendation engine is in thepresentation system and wherein the adaptive recommendation engine is inthe packet selection system.
 15. A method for content selection with arules-based recommendation engine and an adaptive recommendation engine,the method comprising: receiving response data from a user device at oneor more servers comprising a packet selection system and a presentationsystem; automatically providing received response data to a rules-basedrecommendation engine, wherein the rules-based recommendation engine isconfigured to select a next node based on: a current location in thecontent network, potential next nodes, past response data, and one orseveral guard conditions associated with the potential next nodes;alerting an adaptive recommendation engine when a selected next nodecomprises a placeholder node, wherein a placeholder node is associatedwith a database of placeholder content the adaptive recommendationengine performing: retrieving at least one model relevant to selectionof next node content from a model database comprising a plurality ofmodels relating to at least one of: a user skill level or a data packetdifficulty level; and selecting next node content based on an output ofthe at least one model; generating subsequent output based at least inpart on the rules-based recommendation engine when the selected nextnode is determined to not be a placeholder node, the subsequent outputlinked directly to the selected next node.
 16. The method of claim 15,further comprising providing the selected next node content to the userdevice.
 17. The method of claim 16, wherein providing the selected nextnode content to the user device comprises: generating a plurality ofelectrical signals comprising the selected next node content; andsending the electrical signal to the user device.
 18. The method ofclaim 17, wherein the rules-based recommendation engine is in thepresentation system and wherein the adaptive recommendation engine is inthe packet selection system.
 19. The method of claim 17, whereinselecting next node content based on an output of the at least one modelcomprises: identifying the user associated with the user device;retrieving the user history; and identifying potential next nodecontent.
 20. The method of claim 19, wherein selecting next node contentbased on an output of the at least one model comprises: identifying oneor several features of at least one of: the potential next node contentor the user history; extracting the identified one or several features;and inputting some or all of the one or several features into theretrieved at least one model.